AachenDepartment of Computer Science

Technical Report

Selexels: a Conceptual Frameworkfor Pointing Devices with LowExpressiveness

Rafael Ballagas and Jan Borchers

ISSN 0935–3232 · Aachener Informatik Berichte · AIB-2006-16

RWTH Aachen · Department of Computer Science · December 2006

The publications of the Department of Computer Science ofRWTH Aachen Universityare in general accessible through the World Wide Web.

http://aib.informatik.rwth-aachen.de/

Selexels: a Conceptual Framework for Pointing Devices withLow Expressiveness

Rafael Ballagas and Jan Borchers

Lehrstuhl fur Informatik XRWTH Aachen, Germany

Email:{ballagas, borchers}@cs.rwth-aachen.de

Abstract. As human–computer interaction extends beyond the desktop,the need emergesfor new input devices and interaction techniques. However,many novel interaction tech-niques must be prototyped in a proof-of-concept form, and can suffer from lowexpressive-ness: their ability to convey the intended meaning is limited. Wepresent a new conceptualframework based onselexelsthat allows application designers to match the expressivenessof the user interface to that of the input device. This allowsthe user interface to provide afluid user experience despite the limitations of the input device. A user study validates theframework, shows that selexel-based pointing tasks can be modeled using Fitts’ Law, andprovides insights for structuring evaluations of prototype input devices.

1 Introduction

With the era of ubiquitous computing emerging, computing resources are moving off thedesktop and extending into our private and public spaces. One research question is how wewill physically interact with these computing systems: Will there be a standard equivalentof mouse and keyboard in the post-desktop world? To answer this question, researchershave been developing new applications, devices, and interaction techniques for situateddisplays [OPC04]. New input device technologies, such as those integrated with mobilephones, can suffer from low expressiveness. Currently, we are lacking the conceptualframeworks to accommodate input devices with low expressiveness.In this paper, we present a conceptual framework that clearly characterizes theexpressive-nessof relative pointing devices. With this definition, designers will be able to structureuser interfaces to match the expressiveness of the input device by limiting the selectionresolution of the display, where the selection resolution is independent from the displayresolution. To this end, we will introduce the notion ofselexelsthat are (to put it simply)“pixels in selection space.”

2 Motivation

The inspiration behind this work is theSweeptechnique [BRSB05] that uses the cameraon a mobile phone to detect motion of the handset in several dimensions. This techniqueenables many direct manipulation interactions with large public displays, including cursorcontrol. Currently, the technique suffers from low sampling rates and resolution becauseof the limitations of current mobile processors and cameras. With future improvements inmobile processing capabilities, the resolution and sampling rate will improve. However, wewant to develop applications that can be used today that still provide a fluid user experience.(Note that this problem also occurs when someone uses standard desktop input devices witha very high resolution display.)

3 Background

The expressiveness of input devices was first defined by Card et al. [CMR91] as an evalua-tion criterion capturing how well the input conveys the intended meaning. Without explicit

guards, a mismatch of expressiveness can cause problems in the user interface. These prob-lems were formally described by Card et al. using parametersof input devices. TheIn pa-rameter represents the input domain, which describes the physical properties of the world.TheOut parameter represents the output domain set of the input device, which describesthe values that an input device can produce.

“In the design of input devices, an expressiveness problem arises when thenumber of elements in theOut set does not match the number of elements in theIn set to which it is connected. If the projection of theOut set includes elementsthat are not in theIn set, the user can specify illegal values; and if theIn setincludes values that are not in the projection, the user cannot specify legal values.”[CMR91]

For an example of an expressiveness problem, consider a touch panel overlaying a displaywhere the resolution of the touch panel (where transducer values form theOut set) is muchless than the resolution of the display (where pixels form the In domain). If a user wantedto select an individual pixel, he would not be able to expressthat request exactly.The expressiveness problem is closely related to the problem of device precision. Pointingprecision characterizes how small of a target can be conveniently selected with the device.Quantifying the pointing precision of absolute input devices in terms of screen area isrelatively straightforward. However, quantifying the precision of relative pointing devicesis more difficult. Card et al. [CMR91] quantify precision of input devices in terms of bitsusing insights from subjective ratings of the difficulty of pointing tasks using the mousein text editing applications, where the threshold between easy and hard tasks lies betweenselecting a word and selecting a character; selecting a wordis thehardest easy task, andselecting a character is theeasiest hard task. Thus device precision is defined by Card etal. as follows:

“We characterize the precision of a device as theID that requires the sameamount of time as the easiest hard task of the mouse.” [CMR91]

Here, ID is the Fitts’ law index of difficulty of the pointing task measured in bits. Theproblem with this definition is that it requires empirical testing to determine the precisionof the device, limiting its utility as a design tool. In this paper, we propose a new definitionto quantify the precision of relative input devices based ondevice parameters, and showhow this definition can be used to predict changes in precision without empirical testing.

4 The Selexel Approach

Our approach is to match the selection resolution of the userinterface to the expressivenessof the input device. This is accomplished by dividing the screen into atomic selectableelements, orselexels, with a resolution that is independent of the pixel resolution of thescreen (see Fig. 1). By separating selexels from pixels, we adjust the range of motion in theinterface to support a smooth and fluid user experience for the input device in use, whilepreserving the screen resolution and information capacityof the display.The traditional conceptual framework for analyzing pointing tasks separates the task intotwo spaces: motor space and display (or visual) space. This is reflected by the frequent useof the control–display (C–D) ratio to describe the relationship between motion distance inthe physical world (meters) and motion distance on the screen (pixels). Our new conceptualframework adds selection space as a level of indirection between motor space and displayspace. Using this framework, a traditional desktop interface is a special case where theselection space is identical to the display space. We note that since the motor space ismapped to selection space and not display space, the conceptof C–D ratio is replacedby the notions of Control-Selection (C–S) ratio for the relationship between motor andselection space, and Selection-Display (S–D) ratio for therelationship between selectionand display space.Expressiveness is easily confused with the notion of deviceresolution. Resolution of aninput device, usually measured in dpi (dots per inch), describes precision in motor space;how small of a movement, in motor space, can be distinguishedby the transducer. Expres-siveness, characterized by selexels (unitless), describes precision in selection space; howmany distinct positions, in selection space, can one express, or “reach”, using this inputdevice in a given timeframe. The length of this timeframe will be discussed below.

Fig. 1.A sample selexels screen division over a typical desktop interface. It indicates that existing desktopinterfaces may need to be modified to disambiguate selectionwith low precision pointing devices.

5 Related Work

There are many approaches to improving selection performance in pointing tasks that in-crease the size of the cursor. Thearea cursorhas an active selection region that spans ascreen area, rather than a single point. Kabbash and Buxton [KB95] show that selection ofa point-sized target can be accurately modeled using Fitts’law by settingW to the cursorwidth, reducing the index of difficulty for small targets. However, area cursors can over-lap multiple targets, making user selection ambiguous. Worden et al. [WWBH97] proposean improved area cursor with an additional hot spot at its center to disambiguate betweenmultiple closely-spaced targets within the cursor area. This cursor performs better than thestandard point cursor when targets are far apart, and identically to point cursors when tar-gets are closer together. The Bubble cursor [GB05] dynamically resizes the active cursorregion to always encompass exactly one selectable object that is nearest to the center posi-tion of the cursor. This effectively changes the width of thetarget to the size of the Voronoiregion surrounding the target, maximizing its size in motorspace. This technique is supe-rior to previous techniques in that it also demonstrates benefits over the point cursor fordensely packed targets. However, for each of these solutions, cursor motion is governed bypixels, making them unsuitable for tasks for input devices with low expressiveness.A cursor in the selexel domain is similar to these techniquesin that it has an active regionthat spans a screen area, rather than a single pixel. However, a cursor highlighting a singleselexel is actually a point cursor in selection space, even though the active region may spana screen region in display space. Thus, the techniques proposed above should result in thesame performance improvements if applied to targets in selection space, but we leave theexperimental validation of this theory to future work.Several selection techniques have been shown to increase pointing performance by dynam-ically adjusting the control–display (C–D) ratio [BGBL04,BCHE05], sometimes referredto as cursor acceleration. Pointing performance can be improved by increasing the control–display ratio while approaching the target (and thereby decreasing the distance traveledin motor space), or by decreasing the control–display ratiowhile inside the target (andthereby increasing the target size in motor space). In this paper, we focus on a static C–Sratio. However, these adaptive techniques should still be applicable in the selexels domain

by replacing the C–D ratio with the C–S ratio, but again we leave the validation of thistheory to future work.

6 The Selexels Framework

A selexel cursor has the same ambiguity problem as the area cursor when covering multipletargets, as demonstrated in Fig. 1. There are several options to eliminate this ambiguity:

– The input device could be restricted to one that supports theminimum selexel reso-lution required for the interface to have no cursor overlap of multiple targets. In thisway, the selexels framework allows us to define device criteria for interacting withexisting applications. However, most desktop applications are not compatible with agrid selection scheme, requiring the standard “one selexelper pixel” scenario.

– Alternatively, if a situation requires the use of a low precision pointing device (e.g.,in the aforementioned case of large public display interactions using the Sweep tech-nique), the layout of the interface could be altered to be compatible with the selexelresolution of the input device. This can be accomplished through manual rearrange-ment, or through automatic layout of user interfaces as in [GW04]. In this way, theselexels framework allows us to define graphical layout constraints for applicationsdesigned for low-expressiveness pointing techniques. (See Future Work for more dis-cussion on this topic.)

– Lastly, drawing inspiration from the Bubble Cursor [GB05],the size of the selexelin pixels (the S–D ratio) can be dynamically varied to alwayscontain one and onlyone selectable target, essentially mapping each selexel tothe Voronoi regions of thetargets on the display, and thereby minimizing the number ofselexels in an interface.A simplified 1D version of this approach is embodied by the tabbed interface, wherea user can cycle the selection focus through the selectable items in an interface, oftenby pressing the tab key. The problem with this approach is that the resulting C–D ratio(a combination of C–S and S–D ratios) might be unpredictablefor users, preventingthem from accurately planning their movements. In this paper, we focus on the case ofa constant S–D ratio.

6.1 Expressiveness of Relative Pointing Devices

We characterize the expressiveness of a relative pointing device using the precisionof the input technique based on models of human motor performance including Fitts’law [Fit54, Mac92], and the linear speed-accuracy tradeoff[SZH+79]. The reasoning be-hind our definition can be best explained using the conceptual framework presented byMeyer et al. [MSK+90], which was used to create the stochastic optimized submovementmodel. This model attempts to reconcile the strengths of Fitts’ Law (better suited to modelspatially constrained tasks, such as cursor positioning using a mouse), and the linear speed-accuracy tradeoff [SZH+79] (better suited to model temporally constrained tasks, such ascursor positioning using a joystick to control cursor velocity) into a unified model capableof expressing a wider range of movement tasks. The stochastic optimized submovementmodel is based on the assumption that the subject attempts tohit the center of the targetregion with their first submovement (see Fig. 2). If the primary submovement successfullyacquires the target, then the action terminates. The model anticipates noise in the motorsystem to affect the primary submovement, causing a slight variation from the intendedmovement and the actual result. If a miss occurs, then a secondary corrective submove-ment will be used, and so on. Thus for a pointing task to be valid for this model, the inputdevice must theoretically allow a subject to reach a target in the primary submovement,even though noise in the motor system may require additionalsubmovements before thetarget is successfully acquired.

We define theexpressivenessof a relative pointing device as the number ofdistinct positions a user can express in a single submovement.

The duration of a submovement is defined by the basic human reaction time. Using Card,Moran, and Newell’s human processor model [CNM83], the basic reaction time is approx-imated byTsub = τp + τc + τm, or one cycle for each of the perceptual, cognitive, and

Fig. 2. The optimized dual-submovement model is a variation of the optimized submovement model withtwo submovements. Hypothetical primary submovements are marked with a solid line, secondary sub-movements with a dashed line. (based on Figure 6.8 from [MSK+90].)

motor processors. This equation approximates the time for the user to observe the motionprogress (τp), decide on a correction (τc), and perform the correction (τm). For the averageuser (“middleman” in [CNM83]), this sum would result in an approximate submovementduration of 240ms.We define the expressiveness of a relative input device in terms of the motion throughputof the transducer (D in dots per sec.), and the psychological limits of human informationprocessing in terms of the submovement duration (Tsub in sec.). Motion throughput de-scribes the rate at which motion information can be processed by the input device. Industrytypically reports the device specifications in terms of device resolution (Ndev in dots perinch) and maximum supported rate of motion (vmotion in inches per sec.). We can use theseparameters to define motion throughput as follows:

D := Ndev ∗ vmotion

Alternatively, the motion throughput can be defined using the transducer resolution (Ntrans

in dots per sample) and sampling frequency (fsample in samples per sec.), assuming a singletransducer per axis of motion. To simplify the definition, wefirst define the notion of samplereach (Reachsample in dots) as the maximum selexel distance that can be reached in a singlesample.

Reachsample := .5 ∗ (Ntrans − 1)

The scaling factor (.5) is necessary because the transducerresolution is split into positiveand negative values along the motion axis. One sample is removed to account for the zerovalue which has no motion. If positive and negative measurements are separated into mul-tiple transducers, this equation may require slight adjustments. The motion throughput canthen be alternatively formulated as,

vmotion := fsample ∗ Reachsample/Ndev

D = Ndev ∗ fsample ∗ Reachsample/Ndev

D = fsample ∗ Reachsample .

We further define the submovement reach (Reachsub in dots) as the maximum selexeldistance that can be reached during a single submovement (Tsub).

Reachsub := D ∗ Tsub

We note thatReachsub is measured in dots, which is a unitless quantity relating tothenumber of distinct values that can be expressed during a submovement.Expressiveness can then be defined as the set of points (E) that a user can express in asingle submovement. For a one-dimensional input device, assuming a C–S gain function ofS(t) (i.e. cursor acceleration), we can define expressiveness asfollows:

E := {S(x) :where0 < x < Reachsub andx ∈ Z},

whereZ is the set of all integers. We note that in the case of no C–S gain (S(t) = t), thisreduces to the set of integers between0 andReachsub, and the cardinality of the expres-siveness set (|E|) is Reachsub + 1 .An input device is said to have higher expressiveness when the cardinality of the expres-siveness set (|E|) is greater than that of another device. We also note that changing theC–S gain function fromS(t) = t cannot increase the cardinality of the expressivenessset; instead, it may create unreachable gaps or even decrease expressiveness if multipletransducer values map to the same cursor displacement in selexel space. This theoreticalobservation fits with the findings of Jellinek et al. [JC90] who explored second order cur-sor acceleration using several different C–D ratios on a mouse, but found no performanceimprovements.

6.2 Examples

A case study of specific input devices should serve to illustrate the theoretical definitionwith practical examples. They show how expressiveness can be used as a design metric togauge the suitability of a relative input device for a particular interaction scenario.

Opto-Mechanical Mouse Figure 3 shows the inner workings of an optical-mechanical mouse. The resolution (dpi) of the mouse is determined by the ratios of thephysical dimensions of the ball, the grips, and the discs. The current industry standardfor device resolution (Ndev ) of these mice ranges from 400 to 800 dpi. For this example,assume that the pulse accumulator is capable of storing 8 bits of information for each di-mension of motion, resulting in a transducer resolution (Ntrans ) of 256 possible outputsper sample.For traditional PS/2 mice, common in the 1990s, the default sampling rate under Windows95 / 98 was 40 Hz. Thus, their throughput can be calculated as follows:

D = (Ntrans − 1) ∗ .5 ∗ fsample

= (256 − 1) ∗ .5 ∗ 40

= 5120

Then theirReachsub follows as:

Reachsub = D ∗ Tsub

= 5100 ∗ .240

= 1224

Thus a user would start to have problems with expressivenessusing this device with ascreen resolution of1225 or higher in any dimension.

Optical Mouse As another example, consider the Agilent Technologies ADNS2610optical mouse sensor [Agi04]. It supports a resolution of 400 dpi, and rates of motion up to12 inches per second. Thus, its throughput can be calculatedas follows:

D = Ndev ∗ vmotion

= 400 ∗ 12

= 4800

Fig. 3. An optical-mechanical mouse: (1) Motion across the desktopsurface moves the ball. (2) Gripstransfer the ball movement to turn (3) optical encoding disks. (4) Infrared LEDs shine through the holes.(5) Infrared sensors accumulates light pulses and convertsthem into X, Y motion. (Source: Wikipedia)

Then itsReachsub follows as:

Reachsub = D ∗ Tsub

= 4800 ∗ .240

= 1152

Thus a user would start to have problems with expressivenessusing this device with ascreen resolution higher than1152 in any dimension. Note that if the resolution of thesensor was bumped to 800 dpi, then theReachsub also doubles supporting resolutionsmuch higher than 2000 pixels in either dimension.

Analog Joystick Consider a USB analog joystick (shown in Figure 4) that has a reso-lution Ntrans of 256 positions on each axis after digital conversion. The joystick measuresabsolute tilt in the (rX, rY) dimensions, but is used as a relative positioning device bymap-ping tilt to control the velocity of cursor movement. Positioning a cursor with velocitycontrol is a temporally constrained task. Modern operatingsystems support sampling suchUSB devices at a rate of 125 Hz. Thus the rate of motionD can be expressed as follows:

D = (Ntrans − 1) ∗ .5 ∗ fs

= (256 − 1) ∗ .5 ∗ 125

= 15937.5

Then the resultingReachsub follows as:

Reachsub = D ∗ Tsub

= 15937.5 ∗ .240

= 3825

This indicates that this particular joystick has a very highexpressiveness, notably higherthan the mice examined above. It should be noted that expressiveness does not necessarilyindicate that a device can be used with a higher felicity [CMR91]. To draw conclusionsabout the pointing speed (or device bandwidth), it is still necessary to compare the devicesusing an ISO 9241-9 [ISO00] standard empirical evaluation.However, the expressivenessreveals which pointing tasks are possible for the input device in a single submovement.

Fig. 4. An analog joystick measures absolute tilt of the stick in therX, rY dimensions.

Sweep Sweep [BRSB05] is an experimental input technique that usesthe camera on amobile phone to detect relative motion of the phone. The interaction is intended to supportnovel interactive applications for large public displays.The current implementation of thistechnique detects relative motion in the(X, Y ) dimensions with a sample frequency (fs)of 12.5 Hz and a transducer resolution (Ntrans ) capable of detecting 9 distinct dots (dis-placements) per sample. The actual physical sensor in this example is the camera whichhas a relatively high resolution. However, for the purposesof expressiveness we considerthe output of the motion detection algorithm to be the transducer output since those are theactual values that can affect the cursor. Thus,

D = (Ntrans − 1) ∗ .5 ∗ fs

= (9 − 1) ∗ .5 ∗ 12.5

= 50

Then theReachsub follows as:

Reachsub = D ∗ Tsub

= 50 ∗ .240

= 12

Thus the largest resolution that would not result in expressiveness problems would be13 × 13. This means that the use of this input device with standard desktop resolutionwill result in difficulties because of the severe mismatch inexpressiveness. This matchesthe difficulties expressed in a previous evaluation of the Sweep technique [BRSB05].

6.3 Expressiveness of Absolute Pointing DevicesFor direct surface interaction, such as a touch screen, the expressiveness|E| simply mapsto the resolution of the input surface (dpi) multiplied by its size. TheReachsub , however,can be limited by the physical characteristics of the relevant parts of the human body, suchas arm length.

7 Practical Application of Selexels

Mismatches in expressiveness can disrupt the user exprience. This new method of char-acterizing expressiveness allows us to identify and resolve these mismatches to provide amore fluid experience. Selexels provide a level of abstraction that allows us to reduce theselection resolution of the display to match the expressiveness of the input device, withoutsacrificing valuable display resolution.

7.1 Usage Scenario

We may apply these concepts to interactions in the domain of large public displays, asilllustrated in the following scenario.

While Hans is waiting for his train to Berlin, he notices a large public displayon the platform displaying advertisements and community news. He recognizesthe style of a 2D bar code next to the display. He has previously used a similarbarcode to initiate interaction with a demo display in the T-Mobile showroom. Hetakes a picture of the code (just as he had done in the showroom), and his phoneautomatically connects wirelessly to the display via Bluetooth and transfers infor-mation characterizing its input expressiveness. The advertisement transforms intoan interactive menu with a selexel resolution matched to hisinput device, allowingHans to select from several options including: browsing thenews, checking theweather, or even contributing to an interactive community bulletin board wherepeople post images, video, and text. Hans waves his phone through the air usingthe Sweep technique to select the weather option. A map of Germany appearsand as he navigates the cursor over different regions, the current conditions andforecast are displayed to the right of the map. It looks like the previously forecastafternoon showers are no longer a threat in Berlin. Hans thencalls his wife toinvite her to go to their favorite restaurant for dinner on the quaint cobblestonepatio.Shortly after Hans leaves, Maria wants to check up on her bulletin board post fromlast week about the proposed new theatre to be built next to the train station. Herphone is much older than Hans’s and the expressiveness of theSweep techniqueis much lower. As she connects to the display, the cursor grows and the menuoptions move further apart to adapt the selexel resolution of the interface to matchthat of her phone. Now she is able to use her more limited phoneas a pointingdevice, yet the system still provides a fluid experience.

This scenario shows how expressiveness can be used to tailora user interface on the fly tothe capabilities of the input devices that are used in a particular interaction. These conceptsmay also be useful for scenarios using very high resolution displays such as those describedin [CRM+06].

8 Evaluation

Card, English, and Burr’s [CEB78] seminal work showed that the efficiencyof pointingdevices can be analyzed using Fitts’ law [Fit54, Mac92], which models human motor per-formance by predicting movement time in a pointing task as follows:

MT = a + b ∗ ID

ID = log2(D

W+ 1),

whereD is the target distance,W is the target width, andID is the index of difficulty of thepointing task.a andb are empirically determined constants that vary with the characteristicsof the input device. These empirically determined constants are affected by a wide varietyof input device characteristics including mass, friction,resolution, sampling rate, lag, andC–D gain [Mac92]. Changes in any of these parameters will also change the result of theregression analysis.In a series of experiments, we attempt to characterize how pointing tasks are affected by therelationship between the expressiveness of the input device and the selexel resolution of thedisplay. A custom test program allows us to vary the selexel resolution of the display, andvary the expressiveness of input devices by artificially limiting the resolution and samplingrate. The test application hides the system cursor and displays a point cursor in selexelspace (an area cursor in pixel space) that moves selexel-wise, appearing to jump from oneselexel position to the next. The application was implemented in Objective-C under MacOS X.

C-S (Reachsample) Selexels Pixels S-D C-D

1280 1280 1280 1 1280

256 256 1280 0.2 1280

40 40 1280 0.03125 1280

20 20 1280 0.01563 1280

Table 1. By matching the selexel resolution to the C–S ratio (Reachsample ) we maintain a constant C–Dratio across the test conditions.

9 Experiment 1

Given that the selexel cursor represents a point cursor in selexel space, Fitts’ Law [Fit54]should be a suitable model for predicting target aquisitiontime in pointing tasks. However,the selexel cursor is an area cursor in pixel space. Previousstudies of area cursors havedemonstrated that selection using area cursors lowers the index of difficulty for smallertargets [KB95, WWBH97]. Also, as users have come to expect pixel-wise cursor motion,the selexel-wise motion may impede or annoy the user. Thus, it is important to examinewhether pointing in the selexel paradigm can be modeled by Fitts’ Law. This experiment isspecifically designed to examine if the input device expressiveness affects the empiricallydetermined constants of the input device (a andb).

9.1 User Study Design

Using a within-groups design, users were asked to complete ahorizontal tapping test basedin part on ISO 9241-9 [ISO00]. For each test condition, the same range of target distances(D = 256, 640, and 1024 pixels) and target widths (W = 64 and 128 pixels) were used.To simulate input devices of varying expressiveness, the sampling period of the mouse wasartificially increased to 20ms to guarantee a constant sampling rate for all test conditions.The transducer resolution (Ntrans ) of the input device was varied to have aReachsample of1280, 256, 40, and 20 dots per sample. The selexel resolutionof the test UI was designedto match theReachsample of the input devices with selexel resolutions of1280 × 800,256 × 160, 40 × 25, and20 × 13 selexels respectively (selexel sizes of1 × 1, 5 × 5,32 × 32, and64 × 64 pixels). By matching the selexel resolution of the UI (S–D ratio) tothe transducer resolution of the input device (C–S ratio), the resulting C–D ratio remainseffectively constant (see Table 1) across the different conditions.The cursors were all displayed as point cursors in selexel space, except for the one selexelper pixel condition. In this condition, a5× 5 pixel area cursor was used instead of a1× 1pixel point cursor for visibility. This particular cursor still maintained the other propertiesof a point cursor in pixel space in that it had an active regionof 1× 1 pixel at the center ofthe cursor, and its motion was pixel-wise.The pixel placement for target pairs remained constant across test conditions. However,the pairs of targets were placed such that some were aligned to the selexel boundaries andsome weren’t (depending on the selexel condition). When a target is not aligned to selexelboundaries, its width may span over several selexels even ifits size in pixels is much lessthan a single selexel.Both the ordering for the different expressiveness conditions, and the ordering for the dif-ferentIDs were varied to reduce learning effects in a within-groups study. A fully crosseddesign resulted in a total of 24 combinations ofD, W , and matchedNtrans+ selexel reso-lution pairs. For each combination, 25 target selections were required.

9.2 Participants

10 volunteers (4 females, 6 males) ranging in age from 22 to 27participated in the study.All of the participants were students (8 computer science, and 2 political science) from alocal university.

0 1 2 3 4 5 6 7

0.4

0.6

0.8

1

1.2

1.4

1.6y =0.24496 +0.151x

R2 =0.7143

F =55.0037p =0.01771

Index of difficulty (pixels)

Mo

ve

me

nt

tim

e (

s)

0 1 2 3 4 5 6 7

0.4

0.6

0.8

1

1.2

1.4

1.6y =0.15009 +0.19687x

R2 =0.95902

F =514.8228p =0.0025404

Index of difficulty (selexels)

Mo

ve

me

nt

tim

e (

s)

Fig. 5. (Left) Index of difficulty calculations in terms of display pixels are not well suited for selexel point-ing tasks. (Right) The same data reinterpreted with index ofdifficulty in terms of selexels provides a strongcorrelation. The correlation is strong despite the fact that this data is mixed across selexel conditions,demonstrating that selexel resolution has no impact on device performance as long as the C–D ratio ispreserved. Here the C–D ratio is preserved by matching the expressiveness of the UI to the expressivenessof the input device by changing the selexel resolution.

9.3 Equipment

The tapping test was performed using a Logitech M-BJ58 (800 dpi) USB optical mouse,an Apple 23” Cinema LCD monitor with the display resolution set to1280 × 800, and aPowerMac Dual 2.0 GHz G5 processor.

9.4 Results

The results from this experiment are shown in Figure 5. The graphs indicate that usingthe distance and width in units of pixels results in a valid (p < 0.05), but poor model(R2 = 0.71) for selexel pointing tasks. If the same data is reinterpreted using target dis-tance (D) and target width (W ) in units of selexels, the model becomes much improved(p < 0.005, R2 = 0.959). An analysis of variance (ANOVA) shows a significant effectfor index of difficulty [F (15, 23) = 42.99, p < 0.0003], and no significant effect for theexpressiveness conditions [F (2, 23) = 1.64, p < 0.28].

9.5 Discussion

These results confirm previous findings [KB95] that show thatarea cursors lower the indexof difficulty for small targets, but these results further show that movement time for targetslarger than one pixel can be accurately predicted using units of selexels to calculate theIDfor selexel pointing tasks.Another result of this experiment is that selexel-wise motion had no effect on task comple-tion time. This is demonstrated by the fact that the mixed results can be modeled very well(R2 = 0.959) using a single linear regression.A more significant result from this experiment is that, contrary to what one might expect,transducer resolution (Ntrans in dots per sample) has no effect on task completion time,as long as the C–D ratio is preserved across test conditions.This result has important im-plications for evaluating prototype input techniques withlow transducer resolution, suchas the Sweep technique. As mobile processors and cameras continue to improve, motiondetection will become more powerful, and the resulting transducer resolution will improve.Using this study as a model, an evaluation can be structured such that conclusions canbe made about pointing performance of future mobile phones as the transducer resolution

(dots per sample) continues to rise (assuming all other parameters, such as C–D ratio, re-main the same).The error rates for pointing under selexels were lower than pointing under pixels. This canbe explained using the speed-accuracy trade-off since the target widths effectively expandto the selexel span of the target, reducing the physical accuracy required for the pointingtask.

10 Experiment 2

To further validate our conceptual framework, it is necessary to experimentally verify thenotion ofReachsub , the maximum distance that can be reached in the first submovement.Based on the conceptual framework, the selexel resolution should matchReachsub prevent-ing any target distances from exceeding the maximum distance. This experiment examinesthe effect of target distances exceedingReachsub .

10.1 User Study Design

This experiment was structured as a horizontal tapping test, very similar to Experiment 1.It used a within-groups design, and reused the parameters for target distance (D), targetwidth (W ), and target placement. However, in this experiment we maintained the trans-ducer resolution (Ntrans ) to have a constantReachsample of 20 dots per sample. With asample period of 20 ms, theReachsub can be calculated as follows:

Reachsub = fsample ∗ Reachsample ∗ Tsub

Reachsub = (1/.020) ∗ (20) ∗ .240

Reachsub = 240

ThisReachsub of 240 selexels per submovement was maintained across all conditions.The selexel resolution of the display (and resulting pixel size) was varied as in Experiment1. As a result the C–S ratio remained constant, while the S–D ratio varied, resulting in arange of C–D ratios.The ordering for the different selexel resolutions, and theordering for the differentIDswere varied to reduce learning effects in a within-groups study. A fully crossed design re-sulted in a total of 24 combinations ofD, W , and selexel resolution. For each combination,25 target selections were required.

10.2 Participants

11 volunteers (2 females and 9 males) ranging in age from 22 to29 participated in thestudy. All of the participants were students (8 computer science, 1 political science, and 2undisclosed) from a local university.

10.3 Equipment

The equipment used was the same as Experiment 1.

10.4 Results

The results from this experiment are shown in Figure 6. In thefirst graph, all of the condi-tions are mixed, resulting in data that is unable to be modeled using Fitts’ Law (p = 0.61).In the second graph, the same data from the first graph is reinterpreted by removing condi-tions (marked with an “x”) where the target distance (D) exceeds the submovement reach(Reachsub) to allow comparison with the original data. The second graph results in a dataset that can be modeled by Fitts’ Law (p < 0.05). Using an analysis of variance (ANOVA),a binary ”reach exceeded” variable has a significant effect [F(1,23)=28.79, p¡0.00003]showing that target distances exceeding submovement reach(Reachsub) increase task com-pletion time.

0 2 4 6

0.5

1

1.5

2

2.5

3

3.5

4

4.5 y =!0.73654 +0.57139x

R2 =0.44928

F =17.9478p =0.61384

Index of difficulty (selexels)

Movem

ent tim

e (

s)

0 2 4 6

0.5

1

1.5

2

2.5

3

3.5

4

4.5 y =!0.057842 +0.29371x

R2 =0.78865

F =59.7046p =0.030317

Index of difficulty (selexels)

Movem

ent tim

e (

s)

Fig. 6. (Left) In this experiment, contrary to the previous experiment, the mixed results across experimentalconditions cannot be modeled with Fitts’ Law. (Right) The same data is shown with target distances greaterthanReachsub removed (marked as “x”). The conditions where the targets were placed only slightly further(256 selexels) than the submovement reach (240 selexels) are still relatively close to the model, indicatingthat slight mismatches in expressiveness are only a minor issue. However, the extreme mismatches inexpressiveness with target distances of (512, 1024) are extreme outliers in terms of target acquisition time.With these points removed, the data can be modeled by Fitts’ Law (p < 0.05). These graphs combinedindicate that an upper bound to the validity of Fitts’ Law canbe predicted.

10.5 Discussion

In the second graph of Figure 6, the conditions where the targets were placed only slightlyfurther (256 selexels) than the submovement reach (240 selexels) are still relatively closeto the other samples, indicating that slight mismatches in expressiveness are only a minorissue. However, the extreme mismatches in expressiveness with target distances of (512,1024) are extreme outliers in terms of target acquisition time. This indicates that targetdistances exceedingReachsub disrupt the user experience and increase task performancetime.The reader may have noticed that the quality of the input device model (R2 = 0.788) ismuch lower than that of the previous experiment. This is due to the fact that the data hasmixed C–D ratios across the different experimental conditions. As mentioned before, theC–D ratio is one of the input device parameters that is captured by the Fitts’ law coefficients(a andb). Separating the different C–D ratio conditions, as in Figure 7, should result inmodels that account for much more of the variance in the data (a higherR2). Figure 7demonstrates that this is true except for the condition witha selexel size of1 × 1, whichcontains only target distances greater thanReachsub . This further indicates that an upperbound to the validity of Fitts’ Law can be predicted usingReachsub .

11 Conclusions

Previous characterizations of the expressiveness of relative input devices required empiricaltesting, limiting their utility as a design tool. We have presented a conceptual frameworkto characterize the expressiveness of input devices based on the physical properties of thehardware, allowing its appropriateness for a particular interaction scenario to be more easilyassessed. Our selexel framework allows the user interface to be tailored (even adapted atrun-time) to match the expressiveness of the input device without sacrificing the screenresolution, which is important to preserve the informationcapacity of the display.Our experiments have shown that pointing under selexels canbe modeled by Fitts’ Law,demonstrating that selexel-based motion has no effect on task performance time. Experi-

1 1.5 2 2.5 3 3.5 4 4.5

3

4

5

6

y =1.8484 +0.9076x

R2 =0.6509

F =7.458p =0.91962

Index of difficulty (selexels)

Movem

ent tim

e (

s)

Selexel size =1x1 pixels

0.8 1 1.2 1.4 1.6 1.8 2

3

4

5

6y =!0.5432 +3.4657x

R2 =0.9416

F =64.492p =0.13784

Index of difficulty (selexels)

Movem

ent tim

e (

s)

Selexel size =5x5 pixels

0.6 0.7 0.8 0.9 1 1.1 1.2

2.5

3

3.5

4

4.5 y =0.052833 +3.9478x

R2 =0.91656

F =43.9387p =0.06133

Index of difficulty (selexels)

Movem

ent tim

e (

s)

Selexel size =32x32 pixels

0.5 0.6 0.7 0.8 0.9 1

1.5

2

2.5

3

3.5

4y =!1.0935 +5.4612x

R2 =0.95928

F =94.2354p =0.051738

Index of difficulty (selexels)

Movem

ent tim

e (

s)

Selexel size =64x64 pixels

Fig. 7. This figure demonstrates that separating the data from Figure 6 into separate charts based on C–Dratio results in more accurate models (a higherR2) for conditions where the targets were withinReachsub .Note that the condition with target distances greater thanReachsub (selexel size of1 × 1) still poorlycorrelates with the model despite separation, further indicating that an upper bound to the validity of Fitts’Law can be predicted.

ment 1 further demonstrates that transducer resolution hasno effect on task performancetime as long as the C–D ratio of the UI is preserved.In experiment 2, our conceptual framework was validated by experimentally verifying thenotion ofReachsub . These results demonstrate that the upper bound for the validity of theFitts’ Law model can be predicted based on the physical properties of the input device.This new conceptual model can help structure the evaluationof input devices with lowexpressiveness.

12 Future work

We are currently working on toolkit support of the selexel framework, allowing applicationsto dynamically adjust their selexel resolution based on theinput device used.This work has focused on cases where the expressiveness of input devices is much lowerthan that of the display. However if the expressiveness of the input device is much higherthan the display, sub-pixel selection is possible, but other feedback mechanisms (externalto the display) are needed to indicate which selexel is selected.The selexels framework has the potential to improve the accessibility of user interfaces bysimplifying pointing tasks for people who have normal visual capabilities, but suffer frommotor impairments.

13 Acknowledgements

We’d like to thank Mahsa Jenabi and Sven Kratz for their help in preparing the user studiesin this work. Also we’d like to thank Michael Rohs, Jennifer Sheridan, and Alan Dix forinsightful conversations that helped shape the notion of selexels and device expressiveness.

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Aachener Informatik-Berichte

This is the list of all technical reports since 1987. To obtain copiesof reports please consulthttp://aib.informatik.rwth-aachen.de/ or send your request to:Informatik-Bibliothek, RWTH Aachen, Ahornstr. 55, 52056 Aachen,Email: biblio@informatik.rwth-aachen.de

1987-01 ∗ Fachgruppe Informatik: Jahresbericht 19861987-02 ∗ David de Frutos Escrig, Klaus Indermark: Equivalence Relations of Non-

Deterministic Ianov-Schemes1987-03 ∗ Manfred Nagl: A Software Development Environment based on Graph

Technology1987-04 ∗ Claus Lewerentz, Manfred Nagl, Bernhard Westfechtel: On Integration

Mechanisms within a Graph-Based Software Development Environment

1987-05 ∗ Reinhard Rinn: Uber Eingabeanomalien bei verschiedenen Inferenzmod-ellen

1987-06 ∗ Werner Damm, Gert Dohmen: Specifying Distributed Computer Archi-tectures in AADL*

1987-07 ∗ Gregor Engels, Claus Lewerentz, Wilhelm Schafer: Graph Grammar En-gineering: A Software Specification Method

1987-08 ∗ Manfred Nagl: Set Theoretic Approaches to Graph Grammars1987-09 ∗ Claus Lewerentz, Andreas Schurr: Experiences with a Database System

for Software Documents1987-10 ∗ Herbert Klaeren, Klaus Indermark: A New Implementation Technique

for Recursive Function Definitions1987-11 ∗ Rita Loogen: Design of a Parallel Programmable Graph Reduction Ma-

chine with Distributed Memory1987-12 J. Borstler, U. Moncke, R. Wilhelm: Table compression for tree automata1988-01 ∗ Gabriele Esser, Johannes Ruckert, Frank Wagner Gesellschaftliche As-

pekte der Informatik1988-02 ∗ Peter Martini, Otto Spaniol: Token-Passing in High-Speed Backbone

Networks for Campus-Wide Environments1988-03 ∗ Thomas Welzel: Simulation of a Multiple Token Ring Backbone1988-04 ∗ Peter Martini: Performance Comparison for HSLAN Media Access Pro-

tocols1988-05 ∗ Peter Martini: Performance Analysis of Multiple Token Rings1988-06 ∗ Andreas Mann, Johannes Ruckert, Otto Spaniol: Datenfunknetze1988-07 ∗ Andreas Mann, Johannes Ruckert: Packet Radio Networks for Data Ex-

change1988-08 ∗ Andreas Mann, Johannes Ruckert: Concurrent Slot Assignment Protocol

for Packet Radio Networks1988-09 ∗ W. Kremer, F. Reichert, J. Ruckert, A. Mann: Entwurf einer Netzw-

erktopologie fur ein Mobilfunknetz zur Unterstutzung des offentlichenStraßenverkehrs

1988-10 ∗ Kai Jakobs: Towards User-Friendly Networking1988-11 ∗ Kai Jakobs: The Directory - Evolution of a Standard1988-12 ∗ Kai Jakobs: Directory Services in Distributed Systems - A Survey1988-13 ∗ Martine Schummer: RS-511, a Protocol for the Plant Floor1988-14 ∗ U. Quernheim: Satellite Communication Protocols - A Performance

Comparison Considering On-Board Processing1988-15 ∗ Peter Martini, Otto Spaniol, Thomas Welzel: File Transfer in High Speed

Token Ring Networks: Performance Evaluation by Approximate Analysisand Simulation

1988-16 ∗ Fachgruppe Informatik: Jahresbericht 19871988-17 ∗ Wolfgang Thomas: Automata on Infinite Objects1988-18 ∗ Michael Sonnenschein: On Petri Nets and Data Flow Graphs1988-19 ∗ Heiko Vogler: Functional Distribution of the Contextual Analysis in

Block-Structured Programming Languages: A Case Study of Tree Trans-ducers

1988-20 ∗ Thomas Welzel: Einsatz des Simulationswerkzeuges QNAP2 zur Leis-tungsbewertung von Kommunikationsprotokollen

1988-21 ∗ Th. Janning, C. Lewerentz: Integrated Project Team Management in aSoftware Development Environment

1988-22 ∗ Joost Engelfriet, Heiko Vogler: Modular Tree Transducers1988-23 ∗ Wolfgang Thomas: Automata and Quantifier Hierarchies1988-24 ∗ Uschi Heuter: Generalized Definite Tree Languages1989-01 ∗ Fachgruppe Informatik: Jahresbericht 19881989-02 ∗ G. Esser, J. Ruckert, F. Wagner (Hrsg.): Gesellschaftliche Aspekte der

Informatik1989-03 ∗ Heiko Vogler: Bottom-Up Computation of Primitive Recursive Tree

Functions1989-04 ∗ Andy Schurr: Introduction to PROGRESS, an Attribute Graph Gram-

mar Based Specification Language1989-05 J. Borstler: Reuse and Software Development - Problems, Solutions, and

Bibliography (in German)1989-06 ∗ Kai Jakobs: OSI - An Appropriate Basis for Group Communication?1989-07 ∗ Kai Jakobs: ISO’s Directory Proposal - Evolution, Current Status and

Future Problems1989-08 ∗ Bernhard Westfechtel: Extension of a Graph Storage for Software Doc-

uments with Primitives for Undo/Redo and Revision Control1989-09 ∗ Peter Martini: High Speed Local Area Networks - A Tutorial1989-10 ∗ P. Davids, Th. Welzel: Performance Analysis of DQDB Based on Simu-

lation1989-11 ∗ Manfred Nagl (Ed.): Abstracts of Talks presented at the WG ’89 15th

International Workshop on Graphtheoretic Concepts in Computer Sci-ence

1989-12 ∗ Peter Martini: The DQDB Protocol - Is it Playing the Game?1989-13 ∗ Martine Schummer: CNC/DNC Communication with MAP1989-14 ∗ Martine Schummer: Local Area Networks for Manufactoring Environ-

ments with hard Real-Time Requirements1989-15 ∗ M. Schummer, Th. Welzel, P. Martini: Integration of Field Bus and

MAP Networks - Hierarchical Communication Systems in ProductionEnvironments

1989-16 ∗ G. Vossen, K.-U. Witt: SUXESS: Towards a Sound Unification of Ex-tensions of the Relational Data Model

1989-17 ∗ J. Derissen, P. Hruschka, M.v.d. Beeck, Th. Janning, M. Nagl: Integrat-ing Structured Analysis and Information Modelling

1989-18 A. Maassen: Programming with Higher Order Functions1989-19 ∗ Mario Rodriguez-Artalejo, Heiko Vogler: A Narrowing Machine for Syn-

tax Directed BABEL1989-20 H. Kuchen, R. Loogen, J.J. Moreno Navarro, M. Rodriguez Artalejo:

Graph-based Implementation of a Functional Logic Language1990-01 ∗ Fachgruppe Informatik: Jahresbericht 19891990-02 ∗ Vera Jansen, Andreas Potthoff, Wolfgang Thomas, Udo Wermuth: A

Short Guide to the AMORE System (Computing Automata, MOnoidsand Regular Expressions)

1990-03 ∗ Jerzy Skurczynski: On Three Hierarchies of Weak SkS Formulas1990-04 R. Loogen: Stack-based Implementation of Narrowing1990-05 H. Kuchen, A. Wagener: Comparison of Dynamic Load Balancing Strate-

gies1990-06 ∗ Kai Jakobs, Frank Reichert: Directory Services for Mobile Communica-

tion1990-07 ∗ Kai Jakobs: What’s Beyond the Interface - OSI Networks to Support

Cooperative Work1990-08 ∗ Kai Jakobs: Directory Names and Schema - An Evaluation1990-09 ∗ Ulrich Quernheim, Dieter Kreuer: Das CCITT - Signalisierungssystem

Nr. 7 auf Satellitenstrecken; Simulation der Zeichengabestrecke1990-11 H. Kuchen, R. Loogen, J.J. Moreno Navarro, M. Rodriguez Artalejo:

Lazy Narrowing in a Graph Machine

1990-12 ∗ Kai Jakobs, Josef Kaltwasser, Frank Reichert, Otto Spaniol: Der Com-puter fahrt mit

1990-13 ∗ Rudolf Mathar, Andreas Mann: Analyzing a Distributed Slot Assign-ment Protocol by Markov Chains

1990-14 A. Maassen: Compilerentwicklung in Miranda - ein Praktikum in funk-tionaler Programmierung (written in german)

1990-15 ∗ Manfred Nagl, Andreas Schurr: A Specification Environment for GraphGrammars

1990-16 A. Schurr: PROGRESS: A VHL-Language Based on Graph Grammars1990-17 ∗ Marita Moller: Ein Ebenenmodell wissensbasierter Konsultationen - Un-

terstutzung fur Wissensakquisition und Erklarungsfahigkeit1990-18 ∗ Eric Kowalewski: Entwurf und Interpretation einer Sprache zur Beschrei-

bung von Konsultationsphasen in Expertensystemen1990-20 Y. Ortega Mallen, D. de Frutos Escrig: A Complete Proof System for

Timed Observations1990-21 ∗ Manfred Nagl: Modelling of Software Architectures: Importance, No-

tions, Experiences1990-22 H. Fassbender, H. Vogler: A Call-by-need Implementation of Syntax Di-

rected Functional Programming1991-01 Guenther Geiler (ed.), Fachgruppe Informatik: Jahresbericht 19901991-03 B. Steffen, A. Ingolfsdottir: Characteristic Formulae for Processes with

Divergence1991-04 M. Portz: A new class of cryptosystems based on interconnection net-

works1991-05 H. Kuchen, G. Geiler: Distributed Applicative Arrays1991-06 ∗ Ludwig Staiger: Kolmogorov Complexity and Hausdorff Dimension1991-07 ∗ Ludwig Staiger: Syntactic Congruences for w-languages1991-09 ∗ Eila Kuikka: A Proposal for a Syntax-Directed Text Processing System1991-10 K. Gladitz, H. Fassbender, H. Vogler: Compiler-based Implementation

of Syntax-Directed Functional Programming1991-11 R. Loogen, St. Winkler: Dynamic Detection of Determinism in Func-

tional Logic Languages1991-12 ∗ K. Indermark, M. Rodriguez Artalejo (Eds.): Granada Workshop on the

Integration of Functional and Logic Programming1991-13 ∗ Rolf Hager, Wolfgang Kremer: The Adaptive Priority Scheduler: A More

Fair Priority Service Discipline1991-14 ∗ Andreas Fasbender, Wolfgang Kremer: A New Approximation Algorithm

for Tandem Networks with Priority Nodes1991-15 J. Borstler, A. Zundorf: Revisiting extensions to Modula-2 to support

reusability1991-16 J. Borstler, Th. Janning: Bridging the gap between Requirements Anal-

ysis and Design1991-17 A. Zundorf, A. Schurr: Nondeterministic Control Structures for Graph

Rewriting Systems1991-18 ∗ Matthias Jarke, John Mylopoulos, Joachim W. Schmidt, Yannis Vassil-

iou: DAIDA: An Environment for Evolving Information Systems1991-19 M. Jeusfeld, M. Jarke: From Relational to Object-Oriented Integrity

Simplification1991-20 G. Hogen, A. Kindler, R. Loogen: Automatic Parallelization of Lazy

Functional Programs1991-21 ∗ Prof. Dr. rer. nat. Otto Spaniol: ODP (Open Distributed Processing):

Yet another Viewpoint1991-22 H. Kuchen, F. Lucking, H. Stoltze: The Topology Description Language

TDL1991-23 S. Graf, B. Steffen: Compositional Minimization of Finite State Systems1991-24 R. Cleaveland, J. Parrow, B. Steffen: The Concurrency Workbench: A

Semantics Based Tool for the Verification of Concurrent Systems1991-25 ∗ Rudolf Mathar, Jurgen Mattfeldt: Optimal Transmission Ranges for Mo-

bile Communication in Linear Multihop Packet Radio Networks

1991-26 M. Jeusfeld, M. Staudt: Query Optimization in Deductive Object Bases1991-27 J. Knoop, B. Steffen: The Interprocedural Coincidence Theorem1991-28 J. Knoop, B. Steffen: Unifying Strength Reduction and Semantic Code

Motion1991-30 T. Margaria: First-Order theories for the verification of complex FSMs1991-31 B. Steffen: Generating Data Flow Analysis Algorithms from Modal Spec-

ifications1992-01 Stefan Eherer (ed.), Fachgruppe Informatik: Jahresbericht 19911992-02 ∗ Bernhard Westfechtel: Basismechanismen zur Datenverwaltung in struk-

turbezogenen Hypertextsystemen1992-04 S. A. Smolka, B. Steffen: Priority as Extremal Probability1992-05 ∗ Matthias Jarke, Carlos Maltzahn, Thomas Rose: Sharing Processes:

Team Coordination in Design Repositories1992-06 O. Burkart, B. Steffen: Model Checking for Context-Free Processes1992-07 ∗ Matthias Jarke, Klaus Pohl: Information Systems Quality and Quality

Information Systems1992-08 ∗ Rudolf Mathar, Jurgen Mattfeldt: Analyzing Routing Strategy NFP in

Multihop Packet Radio Networks on a Line1992-09 ∗ Alfons Kemper, Guido Moerkotte: Grundlagen objektorientierter Daten-

banksysteme1992-10 Matthias Jarke, Manfred Jeusfeld, Andreas Miethsam, Michael Gocek:

Towards a logic-based reconstruction of software configuration manage-ment

1992-11 Werner Hans: A Complete Indexing Scheme for WAM-based AbstractMachines

1992-12 W. Hans, R. Loogen, St. Winkler: On the Interaction of Lazy Evaluationand Backtracking

1992-13 ∗ Matthias Jarke, Thomas Rose: Specification Management with CAD1992-14 Th. Noll, H. Vogler: Top-down Parsing with Simultaneous Evaluation on

Noncircular Attribute Grammars1992-15 A. Schuerr, B. Westfechtel: Graphgrammatiken und Graphersetzungssys-

teme(written in german)1992-16 ∗ Graduiertenkolleg Informatik und Technik (Hrsg.): Forschungsprojekte

des Graduiertenkollegs Informatik und Technik1992-17 M. Jarke (ed.): ConceptBase V3.1 User Manual1992-18 ∗ Clarence A. Ellis, Matthias Jarke (Eds.): Distributed Cooperation in

Integrated Information Systems - Proceedings of the Third InternationalWorkshop on Intelligent and Cooperative Information Systems

1992-19-00 H. Kuchen, R. Loogen (eds.): Proceedings of the 4th Int. Workshop onthe Parallel Implementation of Functional Languages

1992-19-01 G. Hogen, R. Loogen: PASTEL - A Parallel Stack-Based Implementationof Eager Functional Programs with Lazy Data Structures (ExtendedAbstract)

1992-19-02 H. Kuchen, K. Gladitz: Implementing Bags on a Shared Memory MIMD-Machine

1992-19-03 C. Rathsack, S.B. Scholz: LISA - A Lazy Interpreter for a Full-FledgedLambda-Calculus

1992-19-04 T.A. Bratvold: Determining Useful Parallelism in Higher Order Func-tions

1992-19-05 S. Kahrs: Polymorphic Type Checking by Interpretation of Code1992-19-06 M. Chakravarty, M. Kohler: Equational Constraints, Residuation, and

the Parallel JUMP-Machine1992-19-07 J. Seward: Polymorphic Strictness Analysis using Frontiers (Draft Ver-

sion)1992-19-08 D. Gartner, A. Kimms, W. Kluge: pi-Redˆ+ - A Compiling Graph-

Reduction System for a Full Fledged Lambda-Calculus1992-19-09 D. Howe, G. Burn: Experiments with strict STG code1992-19-10 J. Glauert: Parallel Implementation of Functional Languages Using

Small Processes

1992-19-11 M. Joy, T. Axford: A Parallel Graph Reduction Machine1992-19-12 A. Bennett, P. Kelly: Simulation of Multicache Parallel Reduction1992-19-13 K. Langendoen, D.J. Agterkamp: Cache Behaviour of Lazy Functional

Programs (Working Paper)1992-19-14 K. Hammond, S. Peyton Jones: Profiling scheduling strategies on the

GRIP parallel reducer1992-19-15 S. Mintchev: Using Strictness Information in the STG-machine1992-19-16 D. Rushall: An Attribute Grammar Evaluator in Haskell1992-19-17 J. Wild, H. Glaser, P. Hartel: Statistics on storage management in a lazy

functional language implementation1992-19-18 W.S. Martins: Parallel Implementations of Functional Languages1992-19-19 D. Lester: Distributed Garbage Collection of Cyclic Structures (Draft

version)1992-19-20 J.C. Glas, R.F.H. Hofman, W.G. Vree: Parallelization of Branch-and-

Bound Algorithms in a Functional Programming Environment1992-19-21 S. Hwang, D. Rushall: The nu-STG machine: a parallelized Spineless

Tagless Graph Reduction Machine in a distributed memory architecture(Draft version)

1992-19-22 G. Burn, D. Le Metayer: Cps-Translation and the Correctness of Opti-mising Compilers

1992-19-23 S.L. Peyton Jones, P. Wadler: Imperative functional programming (Briefsummary)

1992-19-24 W. Damm, F. Liu, Th. Peikenkamp: Evaluation and Parallelization ofFunctions in Functional + Logic Languages (abstract)

1992-19-25 M. Kesseler: Communication Issues Regarding Parallel Functional GraphRewriting

1992-19-26 Th. Peikenkamp: Charakterizing and representing neededness in func-tional loginc languages (abstract)

1992-19-27 H. Doerr: Monitoring with Graph-Grammars as formal operational Mod-els

1992-19-28 J. van Groningen: Some implementation aspects of Concurrent Clean ondistributed memory architectures

1992-19-29 G. Ostheimer: Load Bounding for Implicit Parallelism (abstract)1992-20 H. Kuchen, F.J. Lopez Fraguas, J.J. Moreno Navarro, M. Rodriguez

Artalejo: Implementing Disequality in a Lazy Functional Logic Language1992-21 H. Kuchen, F.J. Lopez Fraguas: Result Directed Computing in a Func-

tional Logic Language1992-22 H. Kuchen, J.J. Moreno Navarro, M.V. Hermenegildo: Independent

AND-Parallel Narrowing1992-23 T. Margaria, B. Steffen: Distinguishing Formulas for Free1992-24 K. Pohl: The Three Dimensions of Requirements Engineering1992-25 ∗ R. Stainov: A Dynamic Configuration Facility for Multimedia Commu-

nications1992-26 ∗ Michael von der Beeck: Integration of Structured Analysis and Timed

Statecharts for Real-Time and Concurrency Specification1992-27 W. Hans, St. Winkler: Aliasing and Groundness Analysis of Logic Pro-

grams through Abstract Interpretation and its Safety1992-28 ∗ Gerhard Steinke, Matthias Jarke: Support for Security Modeling in In-

formation Systems Design1992-29 B. Schinzel: Warum Frauenforschung in Naturwissenschaft und Technik1992-30 A. Kemper, G. Moerkotte, K. Peithner: Object-Orientation Axiomatised

by Dynamic Logic1992-32 ∗ Bernd Heinrichs, Kai Jakobs: Timer Handling in High-Performance

Transport Systems1992-33 ∗ B. Heinrichs, K. Jakobs, K. Lenßen, W. Reinhardt, A. Spinner: Euro-

Bridge: Communication Services for Multimedia Applications1992-34 C. Gerlhof, A. Kemper, Ch. Kilger, G. Moerkotte: Partition-Based Clus-

tering in Object Bases: From Theory to Practice1992-35 J. Borstler: Feature-Oriented Classification and Reuse in IPSEN

1992-36 M. Jarke, J. Bubenko, C. Rolland, A. Sutcliffe, Y. Vassiliou: Theories Un-derlying Requirements Engineering: An Overview of NATURE at Gen-esis

1992-37 ∗ K. Pohl, M. Jarke: Quality Information Systems: Repository Support forEvolving Process Models

1992-38 A. Zuendorf: Implementation of the imperative / rule based languagePROGRES

1992-39 P. Koch: Intelligentes Backtracking bei der Auswertung funktional-logischer Programme

1992-40 ∗ Rudolf Mathar, Jurgen Mattfeldt: Channel Assignment in Cellular RadioNetworks

1992-41 ∗ Gerhard Friedrich, Wolfgang Neidl: Constructive Utility in Model-BasedDiagnosis Repair Systems

1992-42 ∗ P. S. Chen, R. Hennicker, M. Jarke: On the Retrieval of Reusable Soft-ware Components

1992-43 W. Hans, St.Winkler: Abstract Interpretation of Functional Logic Lan-guages

1992-44 N. Kiesel, A. Schuerr, B. Westfechtel: Design and Evaluation of GRAS,a Graph-Oriented Database System for Engineering Applications

1993-01 ∗ Fachgruppe Informatik: Jahresbericht 19921993-02 ∗ Patrick Shicheng Chen: On Inference Rules of Logic-Based Information

Retrieval Systems1993-03 G. Hogen, R. Loogen: A New Stack Technique for the Management of

Runtime Structures in Distributed Environments1993-05 A. Zundorf: A Heuristic for the Subgraph Isomorphism Problem in Ex-

ecuting PROGRES1993-06 A. Kemper, D. Kossmann: Adaptable Pointer Swizzling Strategies in

Object Bases: Design, Realization, and Quantitative Analysis1993-07 ∗ Graduiertenkolleg Informatik und Technik (Hrsg.): Graduiertenkolleg In-

formatik und Technik1993-08 ∗ Matthias Berger: k-Coloring Vertices using a Neural Network with Con-

vergence to Valid Solutions1993-09 M. Buchheit, M. Jeusfeld, W. Nutt, M. Staudt: Subsumption between

Queries to Object-Oriented Databases1993-10 O. Burkart, B. Steffen: Pushdown Processes: Parallel Composition and

Model Checking1993-11 ∗ R. Große-Wienker, O. Hermanns, D. Menzenbach, A. Pollacks, S. Repet-

zki, J. Schwartz, K. Sonnenschein, B. Westfechtel: Das SUKITS-Projekt:A-posteriori-Integration heterogener CIM-Anwendungssysteme

1993-12 ∗ Rudolf Mathar, Jurgen Mattfeldt: On the Distribution of CumulatedInterference Power in Rayleigh Fading Channels

1993-13 O. Maler, L. Staiger: On Syntactic Congruences for omega-languages1993-14 M. Jarke, St. Eherer, R. Gallersdoerfer, M. Jeusfeld, M. Staudt: Con-

ceptBase - A Deductive Object Base Manager1993-15 M. Staudt, H.W. Nissen, M.A. Jeusfeld: Query by Class, Rule and Con-

cept1993-16 ∗ M. Jarke, K. Pohl, St. Jacobs et al.: Requirements Engineering: An In-

tegrated View of Representation Process and Domain1993-17 ∗ M. Jarke, K. Pohl: Establishing Vision in Context: Towards a Model of

Requirements Processes1993-18 W. Hans, H. Kuchen, St. Winkler: Full Indexing for Lazy Narrowing1993-19 W. Hans, J.J. Ruz, F. Saenz, St. Winkler: A VHDL Specification of a

Shared Memory Parallel Machine for Babel1993-20 ∗ K. Finke, M. Jarke, P. Szczurko, R. Soltysiak: Quality Management for

Expert Systems in Process Control1993-21 M. Jarke, M.A. Jeusfeld, P. Szczurko: Three Aspects of Intelligent Co-

operation in the Quality Cycle1994-01 Margit Generet, Sven Martin (eds.), Fachgruppe Informatik: Jahres-

bericht 1993

1994-02 M. Lefering: Development of Incremental Integration Tools Using FormalSpecifications

1994-03 ∗ P. Constantopoulos, M. Jarke, J. Mylopoulos, Y. Vassiliou: The SoftwareInformation Base: A Server for Reuse

1994-04 ∗ Rolf Hager, Rudolf Mathar, Jurgen Mattfeldt: Intelligent Cruise Controland Reliable Communication of Mobile Stations

1994-05 ∗ Rolf Hager, Peter Hermesmann, Michael Portz: Feasibility of Authenti-cation Procedures within Advanced Transport Telematics

1994-06 ∗ Claudia Popien, Bernd Meyer, Axel Kuepper: A Formal Approach toService Import in ODP Trader Federations

1994-07 P. Peters, P. Szczurko: Integrating Models of Quality Management Meth-ods by an Object-Oriented Repository

1994-08 ∗ Manfred Nagl, Bernhard Westfechtel: A Universal Component for theAdministration in Distributed and Integrated Development Environ-ments

1994-09 ∗ Patrick Horster, Holger Petersen: Signatur- und Authentifikationsver-fahren auf der Basis des diskreten Logarithmusproblems

1994-11 A. Schurr: PROGRES, A Visual Language and Environment for PRO-gramming with Graph REwrite Systems

1994-12 A. Schurr: Specification of Graph Translators with Triple Graph Gram-mars

1994-13 A. Schurr: Logic Based Programmed Structure Rewriting Systems1994-14 L. Staiger: Codes, Simplifying Words, and Open Set Condition1994-15 ∗ Bernhard Westfechtel: A Graph-Based System for Managing Configura-

tions of Engineering Design Documents1994-16 P. Klein: Designing Software with Modula-31994-17 I. Litovsky, L. Staiger: Finite acceptance of infinite words1994-18 G. Hogen, R. Loogen: Parallel Functional Implementations: Graphbased

vs. Stackbased Reduction1994-19 M. Jeusfeld, U. Johnen: An Executable Meta Model for Re-Engineering

of Database Schemas1994-20 ∗ R. Gallersdorfer, M. Jarke, K. Klabunde: Intelligent Networks as a Data

Intensive Application (INDIA)1994-21 M. Mohnen: Proving the Correctness of the Static Link Technique Using

Evolving Algebras1994-22 H. Fernau, L. Staiger: Valuations and Unambiguity of Languages, with

Applications to Fractal Geometry1994-24 ∗ M. Jarke, K. Pohl, R. Domges, St. Jacobs, H. W. Nissen: Requirements

Information Management: The NATURE Approach1994-25 ∗ M. Jarke, K. Pohl, C. Rolland, J.-R. Schmitt: Experience-Based Method

Evaluation and Improvement: A Process Modeling Approach1994-26 ∗ St. Jacobs, St. Kethers: Improving Communication and Decision Making

within Quality Function Deployment1994-27 ∗ M. Jarke, H. W. Nissen, K. Pohl: Tool Integration in Evolving Informa-

tion Systems Environments1994-28 O. Burkart, D. Caucal, B. Steffen: An Elementary Bisimulation Decision

Procedure for Arbitrary Context-Free Processes1995-01 ∗ Fachgruppe Informatik: Jahresbericht 19941995-02 Andy Schurr, Andreas J. Winter, Albert Zundorf: Graph Grammar En-

gineering with PROGRES1995-03 Ludwig Staiger: A Tight Upper Bound on Kolmogorov Complexity by

Hausdorff Dimension and Uniformly Optimal Prediction1995-04 Birgitta Konig-Ries, Sven Helmer, Guido Moerkotte: An experimental

study on the complexity of left-deep join ordering problems for cyclicqueries

1995-05 Sophie Cluet, Guido Moerkotte: Efficient Evaluation of Aggregates onBulk Types

1995-06 Sophie Cluet, Guido Moerkotte: Nested Queries in Object Bases1995-07 Sophie Cluet, Guido Moerkotte: Query Optimization Techniques Ex-

ploiting Class Hierarchies

1995-08 Markus Mohnen: Efficient Compile-Time Garbage Collection for Arbi-trary Data Structures

1995-09 Markus Mohnen: Functional Specification of Imperative Programs: AnAlternative Point of View of Functional Languages

1995-10 Rainer Gallersdorfer, Matthias Nicola: Improving Performance in Repli-cated Databases through Relaxed Coherency

1995-11 ∗ M.Staudt, K.von Thadden: Subsumption Checking in Knowledge Bases1995-12 ∗ G.V.Zemanek, H.W.Nissen, H.Hubert, M.Jarke: Requirements Analy-

sis from Multiple Perspectives: Experiences with Conceptual ModelingTechnology

1995-13 ∗ M.Staudt, M.Jarke: Incremental Maintenance of Externally MaterializedViews

1995-14 ∗ P.Peters, P.Szczurko, M.Jeusfeld: Oriented Information Management:Conceptual Models at Work

1995-15 ∗ Matthias Jarke, Sudha Ram (Hrsg.): WITS 95 Proceedings of the 5thAnnual Workshop on Information Technologies and Systems

1995-16 ∗ W.Hans, St.Winkler, F.Saenz: Distributed Execution in Functional LogicProgramming

1996-01 ∗ Jahresbericht 19951996-02 Michael Hanus, Christian Prehofer: Higher-Order Narrowing with Defi-

nitional Trees1996-03 ∗ W.Scheufele, G.Moerkotte: Optimal Ordering of Selections and Joins in

Acyclic Queries with Expensive Predicates1996-04 Klaus Pohl: PRO-ART: Enabling Requirements Pre-Traceability1996-05 Klaus Pohl: Requirements Engineering: An Overview1996-06 ∗ M.Jarke, W.Marquardt: Design and Evaluation of Computer–Aided Pro-

cess Modelling Tools1996-07 Olaf Chitil: The Sigma-Semantics: A Comprehensive Semantics for Func-

tional Programs1996-08 ∗ S.Sripada: On Entropy and the Limitations of the Second Law of Ther-

modynamics1996-09 Michael Hanus (Ed.): Proceedings of the Poster Session of ALP96 - Fifth

International Conference on Algebraic and Logic Programming1996-09-0 Michael Hanus (Ed.): Proceedings of the Poster Session of ALP 96 -

Fifth International Conference on Algebraic and Logic Programming:Introduction and table of contents

1996-09-1 Ilies Alouini: An Implementation of Conditional Concurrent Rewritingon Distributed Memory Machines

1996-09-2 Olivier Danvy, Karoline Malmkjær: On the Idempotence of the CPSTransformation

1996-09-3 Vıctor M. Gulıas, Jose L. Freire: Concurrent Programming in Haskell1996-09-4 Sebastien Limet, Pierre Rety: On Decidability of Unifiability Modulo

Rewrite Systems1996-09-5 Alexandre Tessier: Declarative Debugging in Constraint Logic Program-

ming1996-10 Reidar Conradi, Bernhard Westfechtel: Version Models for Software Con-

figuration Management1996-11 ∗ C.Weise, D.Lenzkes: A Fast Decision Algorithm for Timed Refinement1996-12 ∗ R.Domges, K.Pohl, M.Jarke, B.Lohmann, W.Marquardt: PRO-

ART/CE* — An Environment for Managing the Evolution of ChemicalProcess Simulation Models

1996-13 ∗ K.Pohl, R.Klamma, K.Weidenhaupt, R.Domges, P.Haumer, M.Jarke: AFramework for Process-Integrated Tools

1996-14 ∗ R.Gallersdorfer, K.Klabunde, A.Stolz, M.Eßmajor: INDIA — IntelligentNetworks as a Data Intensive Application, Final Project Report, June1996

1996-15 ∗ H.Schimpe, M.Staudt: VAREX: An Environment for Validating and Re-fining Rule Bases

1996-16 ∗ M.Jarke, M.Gebhardt, S.Jacobs, H.Nissen: Conflict Analysis Across Het-erogeneous Viewpoints: Formalization and Visualization

1996-17 Manfred A. Jeusfeld, Tung X. Bui: Decision Support Components on theInternet

1996-18 Manfred A. Jeusfeld, Mike Papazoglou: Information Brokering: Design,Search and Transformation

1996-19 ∗ P.Peters, M.Jarke: Simulating the impact of information flows in net-worked organizations

1996-20 Matthias Jarke, Peter Peters, Manfred A. Jeusfeld: Model-driven plan-ning and design of cooperative information systems

1996-21 ∗ G.de Michelis, E.Dubois, M.Jarke, F.Matthes, J.Mylopoulos, K.Pohl,J.Schmidt, C.Woo, E.Yu: Cooperative information systems: a manifesto

1996-22 ∗ S.Jacobs, M.Gebhardt, S.Kethers, W.Rzasa: Filling HTML forms simul-taneously: CoWeb architecture and functionality

1996-23 ∗ M.Gebhardt, S.Jacobs: Conflict Management in Design1997-01 Michael Hanus, Frank Zartmann (eds.): Jahresbericht 19961997-02 Johannes Faassen: Using full parallel Boltzmann Machines for Optimiza-

tion1997-03 Andreas Winter, Andy Schurr: Modules and Updatable Graph Views for

PROgrammed Graph REwriting Systems1997-04 Markus Mohnen, Stefan Tobies: Implementing Context Patterns in the

Glasgow Haskell Compiler1997-05 ∗ S.Gruner: Schemakorrespondenzaxiome unterstutzen die paargramma-

tische Spezifikation inkrementeller Integrationswerkzeuge1997-06 Matthias Nicola, Matthias Jarke: Design and Evaluation of Wireless

Health Care Information Systems in Developing Countries1997-07 Petra Hofstedt: Taskparallele Skelette fur irregular strukturierte Prob-

leme in deklarativen Sprachen1997-08 Dorothea Blostein, Andy Schurr: Computing with Graphs and Graph

Rewriting1997-09 Carl-Arndt Krapp, Bernhard Westfechtel: Feedback Handling in Dy-

namic Task Nets1997-10 Matthias Nicola, Matthias Jarke: Integrating Replication and Commu-

nication in Performance Models of Distributed Databases1997-11 ∗ R. Klamma, P. Peters, M. Jarke: Workflow Support for Failure Manage-

ment in Federated Organizations1997-13 Markus Mohnen: Optimising the Memory Management of Higher-Order

Functional Programs1997-14 Roland Baumann: Client/Server Distribution in a Structure-Oriented

Database Management System1997-15 George Botorog: High-Level Parallel Programming and the Efficient Im-

plementation of Numerical Algorithms1998-01 ∗ Fachgruppe Informatik: Jahresbericht 19971998-02 Stefan Gruner, Manfred Nagel, Andy Schurr: Fine-grained and

Structure-Oriented Document Integration Tools are Needed for Devel-opment Processes

1998-03 Stefan Gruner: Einige Anmerkungen zur graphgrammatischen Spezifika-tion von Integrationswerkzeugen nach Westfechtel, Janning, Lefering undSchurr

1998-04 ∗ O. Kubitz: Mobile Robots in Dynamic Environments1998-05 Martin Leucker, Stephan Tobies: Truth - A Verification Platform for

Distributed Systems1998-06 ∗ Matthias Oliver Berger: DECT in the Factory of the Future1998-07 M. Arnold, M. Erdmann, M. Glinz, P. Haumer, R. Knoll, B. Paech, K.

Pohl, J. Ryser, R. Studer, K. Weidenhaupt: Survey on the Scenario Usein Twelve Selected Industrial Projects

1998-09 ∗ Th. Lehmann: Geometrische Ausrichtung medizinischer Bilder amBeispiel intraoraler Radiographien

1998-10 ∗ M. Nicola, M. Jarke: Performance Modeling of Distributed and Repli-cated Databases

1998-11 ∗ Ansgar Schleicher, Bernhard Westfechtel, Dirk Jager: Modeling DynamicSoftware Processes in UML

1998-12 ∗ W. Appelt, M. Jarke: Interoperable Tools for Cooperation Support usingthe World Wide Web

1998-13 Klaus Indermark: Semantik rekursiver Funktionsdefinitionen mit Strik-theitsinformation

1999-01 ∗ Jahresbericht 19981999-02 ∗ F. Huch: Verifcation of Erlang Programs using Abstract Interpretation

and Model Checking — Extended Version1999-03 ∗ R. Gallersdorfer, M. Jarke, M. Nicola: The ADR Replication Manager1999-04 Marıa Alpuente, Michael Hanus, Salvador Lucas, German Vidal: Spe-

cialization of Functional Logic Programs Based on Needed Narrowing1999-05 ∗ W. Thomas (Ed.): DLT 99 - Developments in Language Theory Fourth

International Conference1999-06 ∗ Kai Jakobs, Klaus-Dieter Kleefeld: Informationssysteme fur die ange-

wandte historische Geographie1999-07 Thomas Wilke: CTL+ is exponentially more succinct than CTL1999-08 Oliver Matz: Dot-Depth and Monadic Quantifier Alternation over Pic-

tures2000-01 ∗ Jahresbericht 19992000-02 Jens Voge, Marcin Jurdzinski A Discrete Strategy Improvement Algo-

rithm for Solving Parity Games2000-03 D. Jager, A. Schleicher, B. Westfechtel: UPGRADE: A Framework for

Building Graph-Based Software Engineering Tools2000-04 Andreas Becks, Stefan Sklorz, Matthias Jarke: Exploring the Semantic

Structure of Technical Document Collections: A Cooperative SystemsApproach

2000-05 Mareike Schoop: Cooperative Document Management2000-06 Mareike Schoop, Christoph Quix (eds.): Proceedings of the Fifth Interna-

tional Workshop on the Language-Action Perspective on CommunicationModelling

2000-07 ∗ Markus Mohnen, Pieter Koopman (Eds.): Proceedings of the 12th Inter-national Workshop of Functional Languages

2000-08 Thomas Arts, Thomas Noll: Verifying Generic Erlang Client-Server Im-plementations

2001-01 ∗ Jahresbericht 20002001-02 Benedikt Bollig, Martin Leucker: Deciding LTL over Mazurkiewicz

Traces2001-03 Thierry Cachat: The power of one-letter rational languages2001-04 Benedikt Bollig, Martin Leucker, Michael Weber: Local Parallel Model

Checking for the Alternation Free mu-Calculus2001-05 Benedikt Bollig, Martin Leucker, Thomas Noll: Regular MSC Languages2001-06 Achim Blumensath: Prefix-Recognisable Graphs and Monadic Second-

Order Logic2001-07 Martin Grohe, Stefan Wohrle: An Existential Locality Theorem2001-08 Mareike Schoop, James Taylor (eds.): Proceedings of the Sixth Interna-

tional Workshop on the Language-Action Perspective on CommunicationModelling

2001-09 Thomas Arts, Jurgen Giesl: A collection of examples for termination ofterm rewriting using dependency pairs

2001-10 Achim Blumensath: Axiomatising Tree-interpretable Structures2001-11 Klaus Indermark, Thomas Noll (eds.): Kolloquium Programmier-

sprachen und Grundlagen der Programmierung2002-01 ∗ Jahresbericht 20012002-02 Jurgen Giesl, Aart Middeldorp: Transformation Techniques for Context-

Sensitive Rewrite Systems2002-03 Benedikt Bollig, Martin Leucker, Thomas Noll: Generalised Regular

MSC Languages2002-04 Jurgen Giesl, Aart Middeldorp: Innermost Termination of Context-

Sensitive Rewriting2002-05 Horst Lichter, Thomas von der Maßen, Thomas Weiler: Modelling Re-

quirements and Architectures for Software Product Lines

2002-06 Henry N. Adorna: 3-Party Message Complexity is Better than 2-PartyOnes for Proving Lower Bounds on the Size of Minimal NondeterministicFinite Automata

2002-07 Jorg Dahmen: Invariant Image Object Recognition using Gaussian Mix-ture Densities

2002-08 Markus Mohnen: An Open Framework for Data-Flow Analysis in Java2002-09 Markus Mohnen: Interfaces with Default Implementations in Java2002-10 Martin Leucker: Logics for Mazurkiewicz traces2002-11 Jurgen Giesl, Hans Zantema: Liveness in Rewriting2003-01 ∗ Jahresbericht 20022003-02 Jurgen Giesl, Rene Thiemann: Size-Change Termination for Term

Rewriting2003-03 Jurgen Giesl, Deepak Kapur: Deciding Inductive Validity of Equations2003-04 Jurgen Giesl, Rene Thiemann, Peter Schneider-Kamp, Stephan Falke:

Improving Dependency Pairs2003-05 Christof Loding, Philipp Rohde: Solving the Sabotage Game is PSPACE-

hard2003-06 Franz Josef Och: Statistical Machine Translation: From Single-Word

Models to Alignment Templates2003-07 Horst Lichter, Thomas von der Maßen, Alexander Nyßen, Thomas

Weiler: Vergleich von Ansatzen zur Feature Modellierung bei der Soft-wareproduktlinienentwicklung

2003-08 Jurgen Giesl, Rene Thiemann, Peter Schneider-Kamp, Stephan Falke:Mechanizing Dependency Pairs

2004-01 ∗ Fachgruppe Informatik: Jahresbericht 20032004-02 Benedikt Bollig, Martin Leucker: Message-Passing Automata are expres-

sively equivalent to EMSO logic2004-03 Delia Kesner, Femke van Raamsdonk, Joe Wells (eds.): HOR 2004 – 2nd

International Workshop on Higher-Order Rewriting2004-04 Slim Abdennadher, Christophe Ringeissen (eds.): RULE 04 – Fifth In-

ternational Workshop on Rule-Based Programming2004-05 Herbert Kuchen (ed.): WFLP 04 – 13th International Workshop on Func-

tional and (Constraint) Logic Programming2004-06 Sergio Antoy, Yoshihito Toyama (eds.): WRS 04 – 4th International

Workshop on Reduction Strategies in Rewriting and Programming2004-07 Michael Codish, Aart Middeldorp (eds.): WST 04 – 7th International

Workshop on Termination2004-08 Klaus Indermark, Thomas Noll: Algebraic Correctness Proofs for Com-

piling Recursive Function Definitions with Strictness Information2004-09 Joachim Kneis, Daniel Molle, Stefan Richter, Peter Rossmanith: Param-

eterized Power Domination Complexity2004-10 Zinaida Benenson, Felix C. Gartner, Dogan Kesdogan: Secure Multi-

Party Computation with Security Modules2005-01 ∗ Fachgruppe Informatik: Jahresbericht 20042005-02 Maximillian Dornseif, Felix C. Gartner, Thorsten Holz, Martin Mink: An

Offensive Approach to Teaching Information Security: “Aachen SummerSchool Applied IT Security”

2005-03 Jurgen Giesl, Rene Thiemann, Peter Schneider-Kamp: Proving and Dis-proving Termination of Higher-Order Functions

2005-04 Daniel Molle, Stefan Richter, Peter Rossmanith: A Faster Algorithm forthe Steiner Tree Problem

2005-05 Fabien Pouget, Thorsten Holz: A Pointillist Approach for ComparingHoneypots

2005-06 Simon Fischer, Berthold Vocking: Adaptive Routing with Stale Informa-tion

2005-07 Felix C. Freiling, Thorsten Holz, Georg Wicherski: Botnet Tracking: Ex-ploring a Root-Cause Methodology to Prevent Distributed Denial-of-Service Attacks

2005-08 Joachim Kneis, Peter Rossmanith: A New Satisfiability Algorithm WithApplications To Max-Cut

2005-09 Klaus Kursawe, Felix C. Freiling: Byzantine Fault Tolerance on GeneralHybrid Adversary Structures

2005-10 Benedikt Bollig: Automata and Logics for Message Sequence Charts2005-11 Simon Fischer, Berthold Vocking: A Counterexample to the Fully Mixed

Nash Equilibrium Conjecture2005-12 Neeraj Mittal, Felix Freiling, S. Venkatesan, Lucia Draque Penso: Ef-

ficient Reductions for Wait-Free Termination Detection in Faulty Dis-tributed Systems

2005-13 Carole Delporte-Gallet, Hugues Fauconnier, Felix C. Freiling: RevisitingFailure Detection and Consensus in Omission Failure Environments

2005-14 Felix C. Freiling, Sukumar Ghosh: Code Stabilization2005-15 Uwe Naumann: The Complexity of Derivative Computation2005-16 Uwe Naumann: Syntax-Directed Derivative Code (Part I: Tangent-

Linear Code)2005-17 Uwe Naumann: Syntax-directed Derivative Code (Part II: Intraprocedu-

ral Adjoint Code)2005-18 Thomas von der Maßen, Klaus Muller, John MacGregor, Eva Geis-

berger, Jorg Dorr, Frank Houdek, Harbhajan Singh, Holger Wußmann,Hans-Veit Bacher, Barbara Paech: Einsatz von Features im Software-Entwicklungsprozess - Abschlußbericht des GI-Arbeitskreises “Features”

2005-19 Uwe Naumann, Andre Vehreschild: Tangent-Linear Code by AugmentedLL-Parsers

2005-20 Felix C. Freiling, Martin Mink: Bericht uber den Workshop zur Ausbil-dung im Bereich IT-Sicherheit Hochschulausbildung, berufliche Weiter-bildung, Zertifizierung von Ausbildungsangeboten am 11. und 12. Au-gust 2005 in Koln organisiert von RWTH Aachen in Kooperation mitBITKOM, BSI, DLR und Gesellschaft fuer Informatik (GI) e.V.

2005-21 Thomas Noll, Stefan Rieger: Optimization of Straight-Line Code Revis-ited

2005-22 Felix Freiling, Maurice Herlihy, Lucia Draque Penso: Optimal Random-ized Fair Exchange with Secret Shared Coins

2005-23 Heiner Ackermann, Alantha Newman, Heiko Roglin, Berthold Vocking:Decision Making Based on Approximate and Smoothed Pareto Curves

2005-24 Alexander Becher, Zinaida Benenson, Maximillian Dornseif: Tamperingwith Motes: Real-World Physical Attacks on Wireless Sensor Networks

2006-01 ∗ Fachgruppe Informatik: Jahresbericht 20052006-03 Michael Maier, Uwe Naumann: Intraprocedural Adjoint Code Generated

by the Differentiation-Enabled NAGWare Fortran Compiler2006-04 Ebadollah Varnik, Uwe Naumann, Andrew Lyons: Toward Low Static

Memory Jacobian Accumulation2006-05 Uwe Naumann, Jean Utke, Patrick Heimbach, Chris Hill, Derya Ozyurt,

Carl Wunsch, Mike Fagan, Nathan Tallent, Michelle Strout: AdjointCode by Source Transformation with OpenAD/F

2006-06 Joachim Kneis, Daniel Molle, Stefan Richter, Peter Rossmanith: Divide-and-Color

2006-07 Thomas Colcombet, Christof Loding: Transforming structures by set in-terpretations

2006-08 Uwe Naumann, Yuxiao Hu: Optimal Vertex Elimination in Single-Expression-Use Graphs

2006-09 Tingting Han, Joost-Pieter Katoen: Counterexamples in ProbabilisticModel Checking

2006-10 Mesut Gunes, Alexander Zimmermann, Martin Wenig, Jan Ritzerfeld,Ulrich Meis: From Simulations to Testbeds - Architecture of the HybridMCG-Mesh Testbed

2006-11 Bastian Schlich, Michael Rohrbach, Michael Weber, Stefan Kowalewski:Model Checking Software for Microcontrollers

2006-12 Benedikt Bollig, Joost-Pieter Katoen, Carsten Kern, Martin Leucker:Replaying Play in and Play out: Synthesis of Design Models from Sce-narios by Learning

2006-13 Wong Karianto, Christof Loding: Unranked Tree Automata with SiblingEqualities and Disequalities

2006-14 Danilo Beuche, Andreas Birk, Heinrich Dreier, Andreas Fleischmann,Heidi Galle, Gerald Heller, Dirk Janzen, Isabel John, Ramin TavakoliKolagari, Thomas von der Maßen, Andreas Wolfram: Report of the GIWork Group “Requirements Management Tools for Product Line Engi-neering”

2006-15 Sebastian Ullrich, Jakob T. Valvoda, Torsten Kuhlen: Utilizing opticalsensors from mice for new input devices

2006-16 Rafael Ballagas, Jan Borchers: Selexels: a Conceptual Framework forPointing Devices with Low Expressiveness

∗ These reports are only available as a printed version.Please contactbiblio@informatik.rwth-aachen.de to obtain copies.