Kaiserslautern - Fachbereich Informatik
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Due to the steadily growing flood of data, the appropriate use of visualizations for efficient data analysis is as important today as it has never been before. In many application domains, the data flood is based on processes that can be represented by node-link diagrams. Within such a diagram, nodes may represent intermediate results (or products), system states (or snapshots), milestones or real (and possibly georeferenced) objects, while links (edges) can embody transition conditions, transformation processes or real physical connections. Inspired by the engineering sciences application domain and the research project “SinOptiKom: Cross-sectoral optimization of transformation processes in municipal infrastructures in rural areas”, a platform for the analysis of transformation processes has been researched and developed based on a geographic information system (GIS). Caused by the increased amount of available and interesting data, a particular challenge is the simultaneous visualization of several visible attributes within one single diagram instead of using multiple ones. Therefore, two approaches have been developed, which utilize the available space between nodes in a diagram to display additional information.
Motivated by the necessity of appropriate result communication with various stakeholders, a concept for a universal, dashboard-based analysis platform has been developed. This web-based approach is conceptually capable of displaying data from various data sources and has been supplemented by collaboration possibilities such as sharing, annotating and presenting features.
In order to demonstrate the applicability and usability of newly developed applications, visualizations or user interfaces, extensive evaluations with human users are often inevitable. To reduce the complexity and the effort for conducting an evaluation, the browser-based evaluation framework (BREF) has been designed and implemented. Through its universal and flexible character, virtually any visualization or interaction running in the browser can be evaluated with BREF without any additional application (except for a modern web browser) on the target device. BREF has already proved itself in a wide range of application areas during the development and has since grown into a comprehensive evaluation tool.
Collaboration aims to increase the efficiency of problem solving and decision making by bringing diverse areas of expertise together, i.e., teams of experts from various disciplines, all necessary to come up with acceptable concepts. This dissertation is concerned with the design of highly efficient computer-supported collaborative work involving active participation of user groups with diverse expertise. Three main contributions can be highlighted: (1) the definition and design of a framework facilitating collaborative decision making; (2) the deployment and evaluation of more natural and intuitive interaction and visualization techniques in order to support multiple decision makers in virtual reality environments; and (3) the integration of novel techniques into a single proof-of-concept system.
Decision making processes are time-consuming, typically involving several iterations of different options before a generally acceptable solution is obtained. Although, collaboration is an often-applied method, the execution of collaborative sessions is often inefficient, does not involve all participants, and decisions are often finalized with- out the agreement of all participants. An increasing number of computer-supported cooperative work systems (CSCW) facilitate collaborative work by providing shared viewpoints and tools to solve joint tasks. However, most of these software systems are designed from a feature-oriented perspective, rather than a human-centered perspective and without the consideration of user groups with diverse experience and joint goals instead of joint tasks. The aim of this dissertation is to bring insights to the following research question: How can computer-supported cooperative work be designed to be more efficient? This question opens up more specific questions like: How can collaborative work be designed to be more efficient? How can all participants be involved in the collaboration process? And how can interaction interfaces that support collaborative work be designed to be more efficient? As such, this dissertation makes contributions in:
1. Definition and design of a framework facilitating decision making and collaborative work. Based on examinations of collaborative work and decision making processes requirements of a collaboration framework are assorted and formulated. Following, an approach to define and rate software/frameworks is introduced. This approach is used to translate the assorted requirements into a software’s architecture design. Next, an approach to evaluate alternatives based on Multi Criteria Decision Making (MCDM) and Multi Attribute Utility Theory (MAUT) is presented. Two case studies demonstrate the usability of this approach for (1) benchmarking between systems and evaluates the value of the desired collaboration framework, and (2) ranking a set of alternatives resulting from a decision-making process incorporating the points of view of multiple stake- holders.
2. Deployment and evaluation of natural and intuitive interaction and visualization techniques in order to support multiple diverse decision makers. A user taxonomy of industrial corporations serves to create a petri network of users in order to identify dependencies and information flows between each other. An explicit characterization and design of task models was developed to define interfaces and further components of the collaboration framework. In order to involve and support user groups with diverse experiences, smart de- vices and virtual reality are used within the presented collaboration framework. Natural and intuitive interaction techniques as well as advanced visualizations of user centered views of the collaboratively processed data are developed in order to support and increase the efficiency of decision making processes. The smartwatch as one of the latest technologies of smart devices, offers new possibilities of interaction techniques. A multi-modal interaction interface is provided, realized with smartwatch and smartphone in full immersive environments, including touch-input, in-air gestures, and speech.
3. Integration of novel techniques into a single proof-of-concept system. Finally, all findings and designed components are combined into the new collaboration framework called IN2CO, for distributed or co-located participants to efficiently collaborate using diverse mobile devices. In a prototypical implementation, all described components are integrated and evaluated. Examples where next-generation network-enabled collaborative environments, connected by visual and mobile interaction devices, can have significant impact are: design and simulation of automobiles and aircrafts; urban planning and simulation of urban infrastructure; or the design of complex and large buildings, including efficiency- and cost-optimized manufacturing buildings as task in factory planning. To demonstrate the functionality and usability of the framework, case studies referring to factory planning are demonstrated. Considering that factory planning is a process that involves the interaction of multiple aspects as well as the participation of experts from different domains (i.e., mechanical engineering, electrical engineering, computer engineering, ergonomics, material science, and even more), this application is suitable to demonstrate the utilization and usability of the collaboration framework. The various software modules and the integrated system resulting from the research will all be subjected to evaluations. Thus, collaborative decision making for co-located and distributed participants is enhanced by the use of natural and intuitive multi-modal interaction interfaces and techniques.
Crowd condition monitoring concerns the crowd safety and concerns business performance metrics. The research problem to be solved is a crowd condition estimation approach to enable and support the supervision of mass events by first-responders and marketing experts, but is also targeted towards supporting social scientists, journalists, historians, public relations experts, community leaders, and political researchers. Real-time insights of the crowd condition is desired for quick reactions and historic crowd conditions measurements are desired for profound post-event crowd condition analysis.
This thesis aims to provide a systematic understanding of different approaches for crowd condition estimation by relying on 2.4 GHz signals and its variation in crowds of people, proposes and categorizes possible sensing approaches, applies supervised machine learning algorithms, and demonstrates experimental evaluation results. I categorize four sensing approaches. Firstly, stationary sensors which are sensing crowd centric signals sources. Secondly, stationary sensors which are sensing other stationary signals sources (either opportunistic or special purpose signal sources). Thirdly, a few volunteers within the crowd equipped with sensors which are sensing other surrounding crowd centric device signals (either individually, in a single group or collaboratively) within a small region. Fourthly, a small subset of participants within the crowd equipped with sensors and roaming throughout a whole city to sense wireless crowd centric signals.
I present and evaluate an approach with meshed stationary sensors which were sensing crowd centric devices. This was demonstrated and empirically evaluated within an industrial project during three of the world-wide largest automotive exhibitions. With over 30 meshed stationary sensors in an optimized setup across 6400m2 I achieved a mean absolute error of the crowd density of just 0.0115
people per square meter which equals to an average of below 6% mean relative error from the ground truth. I validate the contextual crowd condition anomaly detection method during the visit of chancellor Mrs. Merkel and during a large press conference during the exhibition. I present the approach of opportunistically sensing stationary based wireless signal variations and validate this during the Hannover CeBIT exhibition with 80 opportunistic sources with a crowd condition estimation relative error of below 12% relying only on surrounding signals in influenced by humans. Pursuing this approach I present an approach with dedicated signal sources and sensors to estimate the condition of shared office environments. I demonstrate methods being viable to even detect low density static crowds, such as people sitting at their desks, and evaluate this on an eight person office scenario. I present the approach of mobile crowd density estimation by a group of sensors detecting other crowd centric devices in the proximity with a classification accuracy of the crowd density of 66 % (improvement of over 22% over a individual sensor) during the crowded Oktoberfest event. I propose a collaborative mobile sensing approach which makes the system more robust against variations that may result from the background of the people rather than the crowd condition with differential features taking information about the link structure between actively scanning devices, the ratio between values observed by different devices, ratio of discovered crowd devices over time, team-wise diversity of discovered devices, number of semi- continuous device visibility periods, and device visibility durations into account. I validate the approach on multiple experiments including the Kaiserslautern European soccer championship public viewing event and evaluated the collaborative mobile sensing approach with a crowd condition estimation accuracy of 77 % while outperforming previous methods by 21%. I present the feasibility of deploying the wireless crowd condition sensing approach to a citywide scale during an event in Zurich with 971 actively sensing participants and outperformed the reference method by 24% in average.
Optical Character Recognition (OCR) system plays an important role in digitization of data acquired as images from a variety of sources. Although the area is very well explored for Latin languages, some of the languages based on Arabic cursive script are not yet explored. It is due to many factors: Most importantly are the unavailability of proper data sets and complexities posed by cursive scripts. The Pashto language is one of such languages which needs considerable exploration towards OCR. In order to develop such an OCR system, this thesis provides a pioneering study that explores deep learning for the Pashto language in the field of OCR.
The Pashto language is spoken by more than $50$ million people across the world, and it is an active medium both for oral as well as written communication. It is associated with rich literary heritage and contains huge written collection. These written materials present contents of simple to complex nature, and layouts from hand-scribed to printed text. The Pashto language presents mainly two types of complexities (i) generic w.r.t. cursive script, (ii) specific w.r.t. Pashto language. Generic complexities are cursiveness, context dependency, and breaker character anomalies, as well as space anomalies. Pashto specific complexities are variations in shape for a single character and shape similarity for some of the additional Pashto characters. Existing research in the area of Arabic OCR did not lead to an end-to-end solution for the mentioned complexities and therefore could not be generalized to build a sophisticated OCR system for Pashto.
The contribution of this thesis spans in three levels, conceptual level, data level, and practical level. In the conceptual level, we have deeply explored the Pashto language and identified those characters, which are responsible for the challenges mentioned above. In the data level, a comprehensive dataset is introduced containing real images of hand-scribed contents. The dataset is manually transcribed and has the most frequent layout patterns associated with the Pashto language. The practical level contribution provides a bridge, in the form of a complete Pashto OCR system, and connects the outcomes of the conceptual and data levels contributions. The practical contribution comprises of skew detection, text-line segmentation, feature extraction, classification, and post-processing. The OCR module is more strengthened by using deep learning paradigm to recognize Pashto cursive script by the framework of Recursive Neural Networks (RNN). Proposed Pashto text recognition is based on Long Short-Term Memory Network (LSTM) and realizes a character recognition rate of $90.78\%$ on Pashto real hand-scribed images. All these contributions are integrated into an application to provide a flexible and generic End-to-End Pashto OCR system.
The impact of this thesis is not only specific to the Pashto language, but it is also beneficial to other cursive languages like Arabic, Urdu, and Persian e.t.c. The main reason is the Pashto character set, which is a superset of Arabic, Persian, and Urdu languages. Therefore, the conceptual contribution of this thesis provides insight and proposes solutions to almost all generic complexities associated with Arabic, Persian, and Urdu languages. For example, an anomaly caused by breaker characters is deeply analyzed, which is shared among 70 languages, mainly use Arabic script. This thesis presents a solution to this issue and is equally beneficial to almost all Arabic like languages.
The scope of this thesis has two important aspects. First, a social impact, i.e., how a society may benefit from it. The main advantages are to bring the historical and almost vanished document to life and to ensure the opportunities to explore, analyze, translate, share, and understand the contents of Pashto language globally. Second, the advancement and exploration of the technical aspects. Because, this thesis empirically explores the recognition and challenges which are solely related to the Pashto language, both regarding character-set and the materials which present such complexities. Furthermore, the conceptual and practical background of this thesis regarding complexities of Pashto language is very beneficial regarding OCR for other cursive languages.
Asynchronous concurrency is a wide-spread way of writing programs that
deal with many short tasks. It is the programming model behind
event-driven concurrency, as exemplified by GUI applications, where the
tasks correspond to event handlers, web applications based around
JavaScript, the implementation of web browsers, but also of server-side
software or operating systems.
This model is widely used because it provides the performance benefits of
concurrency together with easier programming than multi-threading. While
there is ample work on how to implement asynchronous programs, and
significant work on testing and model checking, little research has been
done on handling asynchronous programs that involve heap manipulation, nor
on how to automatically optimize code for asynchronous concurrency.
This thesis addresses the question of how we can reason about asynchronous
programs while considering the heap, and how to use this this to optimize
programs. The work is organized along the main questions: (i) How can we
reason about asynchronous programs, without ignoring the heap? (ii) How
can we use such reasoning techniques to optimize programs involving
asynchronous behavior? (iii) How can we transfer these reasoning and
optimization techniques to other settings?
The unifying idea behind all the results in the thesis is the use of an
appropriate model encompassing global state and a promise-based model of
asynchronous concurrency. For the first question, We start from refinement
type systems for sequential programs and extend them to perform precise
resource-based reasoning in terms of heap contents, known outstanding
tasks and promises. This extended type system is known as Asynchronous
Liquid Separation Types, or ALST for short. We implement ALST in for OCaml
programs using the Lwt library.
For the second question, we consider a family of possible program
optimizations, described by a set of rewriting rules, the DWFM rules. The
rewriting rules are type-driven: We only guarantee soundness for programs
that are well-typed under ALST. We give a soundness proof based on a
semantic interpretation of ALST that allows us to show behavior inclusion
of pairs of programs.
For the third question, we address an optimization problem from industrial
practice: Normally, JavaScript files that are referenced in an HTML file
are be loaded synchronously, i.e., when a script tag is encountered, the
browser must suspend parsing, then load and execute the script, and only
after will it continue parsing HTML. But in practice, there are numerous
JavaScript files for which asynchronous loading would be perfectly sound.
First, we sketch a hypothetical optimization using the DWFM rules and a
static analysis.
To actually implement the analysis, we modify the approach to use a
dynamic analysis. This analysis, known as JSDefer, enables us to analyze
real-world web pages, and provide experimental evidence for the efficiency
of this transformation.
Embedded reactive systems underpin various safety-critical applications wherein they interact with other systems and the environment with limited or even no human supervision. Therefore, design errors that violate essential system specifications can lead to severe unacceptable damages. For this reason, formal verification of such systems in their physical environment is of high interest. Synchronous programs are typically used to represent embedded reactive systems while hybrid systems serve to model discrete reactive system in a continuous environment. As such, both synchronous programs and hybrid systems play important roles in the model-based design of embedded reactive systems. This thesis develops induction-based techniques for safety property verification of synchronous and hybrid programs. The imperative synchronous language Quartz and its hybrid systems’ extensions are used to sustain the findings.
Deductive techniques for software verification typically use Hoare calculus. In this context, Verification Condition Generation (VCG) is used to apply Hoare calculus rules to a program whose statements are annotated with pre- and postconditions so that the validity of an obtained Verification Condition (VC) implies correctness of a given proof goal. Due to the abstraction of macro steps, Hoare calculus cannot directly generate VCs of synchronous programs unless it handles additional label variables or goto statements. As a first contribution, Floyd’s induction-based approach is employed to generate VCs for synchronous and hybrid programs. Five VCG methods are introduced that use inductive assertions to decompose the overall proof goal. Given the right assertions, the procedure can automatically generate a set of VCs that can then be checked by SMT solvers or automated theorem provers. The methods are proved sound and relatively complete, provided that the underlying assertion language is expressive enough. They can be applied to any program with a state-based semantics.
Property Directed Reachability (PDR) is an efficient method for synchronous hardware circuit verification based on induction rather than fixpoint computation. Crucial steps of the PDR method consist of deciding about the reachability of Counterexamples to Induction (CTIs) and generalizing them to clauses that cover as many unreachable states as possible. The thesis demonstrates that PDR becomes more efficient for imperative synchronous programs when using the distinction between the control- and dataflow. Before calling the PDR method, it is possible to derive additional program control-flow information that can be added to the transition relation such that less CTIs will be generated. Two methods to compute additional control-flow information are presented that differ in how precisely they approximate the reachable control-flow states and, consequently, in their required runtime. After calling the PDR method, the CTI identification work is reduced to its control-flow part and to checking whether the obtained control-flow states are unreachable in the corresponding extended finite state machine of the program. If so, all states of the transition system that refer to the same program locations can be excluded, which significantly increases the performance of PDR.
Analyzing Centrality Indices in Complex Networks: an Approach Using Fuzzy Aggregation Operators
(2018)
The identification of entities that play an important role in a system is one of the fundamental analyses being performed in network studies. This topic is mainly related to centrality indices, which quantify node centrality with respect to several properties in the represented network. The nodes identified in such an analysis are called central nodes. Although centrality indices are very useful for these analyses, there exist several challenges regarding which one fits best
for a network. In addition, if the usage of only one index for determining central
nodes leads to under- or overestimation of the importance of nodes and is
insufficient for finding important nodes, then the question is how multiple indices
can be used in conjunction in such an evaluation. Thus, in this thesis an approach is proposed that includes multiple indices of nodes, each indicating
an aspect of importance, in the respective evaluation and where all the aspects of a node’s centrality are analyzed in an explorative manner. To achieve this
aim, the proposed idea uses fuzzy operators, including a parameter for generating different types of aggregations over multiple indices. In addition, several preprocessing methods for normalization of those values are proposed and discussed. We investigate whether the choice of different decisions regarding the
aggregation of the values changes the ranking of the nodes or not. It is revealed that (1) there are nodes that remain stable among the top-ranking nodes, which
makes them the most central nodes, and there are nodes that remain stable
among the bottom-ranking nodes, which makes them the least central nodes; and (2) there are nodes that show high sensitivity to the choice of normalization
methods and/or aggregations. We explain both cases and the reasons why the nodes’ rankings are stable or sensitive to the corresponding choices in various networks, such as social networks, communication networks, and air transportation networks.
Postmortem Analysis of Decayed Online Social Communities: Cascade Pattern Analysis and Prediction
(2018)
Recently, many online social networks, such as MySpace, Orkut, and Friendster, have faced inactivity decay of their members, which contributed to the collapse of these networks. The reasons, mechanics, and prevention mechanisms of such inactivity decay are not fully understood. In this work, we analyze decayed and alive subwebsites from the Stack Exchange platform. The analysis mainly focuses on the inactivity cascades that occur among the members of these communities. We provide measures to understand the decay process and statistical analysis to extract the patterns that accompany the inactivity decay. Additionally, we predict cascade size and cascade virality using machine learning. The results of this work include a statistically significant difference of the decay patterns between the decayed and the alive subwebsites. These patterns are mainly cascade size, cascade virality, cascade duration, and cascade similarity. Additionally, the contributed prediction framework showed satisfactorily prediction results compared to a baseline predictor. Supported by empirical evidence, the main findings of this work are (1) there are significantly different decay patterns in the alive and the decayed subwebsites of the Stack Exchange; (2) the cascade’s node degrees contribute more to the decay process than the cascade’s virality, which indicates that the expert members of the Stack Exchange subwebsites were mainly responsible for the activity or inactivity of the Stack Exchange subwebsites; (3) the Statistics subwebsite is going through decay dynamics that may lead to it becoming fully-decayed; (4) the decay process is not governed by only one network measure, it is better described using multiple measures; (5) decayed subwebsites were originally less resilient to inactivity decay, unlike the alive subwebsites; and (6) network’s structure in the early stages of its evolution dictates the activity/inactivity characteristics of the network.
The Symbol Grounding Problem (SGP) is one of the first attempts to proposed a hypothesis about mapping abstract concepts and the real world. For example, the concept "ball" can be represented by an object with a round shape (visual modality) and phonemes /b/ /a/ /l/ (audio modality).
This thesis is inspired by the association learning presented in infant development.
Newborns can associate visual and audio modalities of the same concept that are presented at the same time for vocabulary acquisition task.
The goal of this thesis is to develop a novel framework that combines the constraints of the Symbol Grounding Problem and Neural Networks in a simplified scenario of association learning in infants. The first motivation is that the network output can be considered as numerical symbolic features because the attributes of input samples are already embedded. The second motivation is the association between two samples is predefined before training via the same vectorial representation. This thesis proposes to associate two samples and the vectorial representation during training. Two scenarios are considered: sample pair association and sequence pair association.
Three main contributions are presented in this work.
The first contribution is a novel Symbolic Association Model based on two parallel MLPs.
The association task is defined by learning that two instances that represent one concept.
Moreover, a novel training algorithm is defined by matching the output vectors of the MLPs with a statistical distribution for obtaining the relationship between concepts and vectorial representations.
The second contribution is a novel Symbolic Association Model based on two parallel LSTM networks that are trained on weakly labeled sequences.
The definition of association task is extended to learn that two sequences represent the same series of concepts.
This model uses a training algorithm that is similar to MLP-based approach.
The last contribution is a Classless Association.
The association task is defined by learning based on the relationship of two samples that represents the same unknown concept.
In summary, the contributions of this thesis are to extend Artificial Intelligence and Cognitive Computation research with a new constraint that is cognitive motivated. Moreover, two training algorithms with a new constraint are proposed for two cases: single and sequence associations. Besides, a new training rule with no-labels with promising results is proposed.
If gradient based derivative algorithms are used to improve industrial products by reducing their target functions, the derivatives need to be exact.
The last percent of possible improvement, like the efficiency of a turbine, can only be gained if the derivatives are consistent with the solution process that is used in the simulation software.
It is problematic that the development of the simulation software is an ongoing process which leads to the use of approximated derivatives.
If a derivative computation is implemented manually, it will be inconsistent after some time if it is not updated.
This thesis presents a generalized approach which differentiates the whole simulation software with Algorithmic Differentiation (AD), and guarantees a correct and consistent derivative computation after each change to the software.
For this purpose, the variable tagging technique is developed.
The technique checks at run-time if all dependencies, which are used by the derivative algorithms, are correct.
Since it is also necessary to check the correctness of the implementation, a theorem is developed which describes how AD derivatives can be compared.
This theorem is used to develop further methods that can detect and correct errors.
All methods are designed such that they can be applied in real world applications and are used within industrial configurations.
The process described above yields consistent and correct derivatives but the efficiency can still be improved.
This is done by deriving new derivative algorithms.
A fixed-point iterator approach, with a consistent derivation, yields all state of the art algorithms and produces two new algorithms.
These two new algorithms include all implementation details and therefore they produce consistent derivative results.
For detecting hot spots in the application, the state of the art techniques are presented and extended.
The data management is changed such that the performance of the software is affected only marginally when quantities, like the number of input and output variables or the memory consumption, are computed for the detection.
The hot spots can be treated with techniques like checkpointing or preaccumulation.
How these techniques change the time and memory consumption is analyzed and it is shown how they need to be used in selected AD tools.
As a last step, the used AD tools are analyzed in more detail.
The major implementation strategies for operator overloading AD tools are presented and implementation improvements for existing AD tools are discussed.
The discussion focuses on a minimal memory consumption and makes it possible to compare AD tools on a theoretical level.
The new AD tool CoDiPack is based on these findings and its design and concepts are presented.
The improvements and findings in this thesis make it possible, that an automatic, consistent and correct derivative is generated in an efficient way for industrial applications.
Mobility has become an integral feature of many wireless networks. Along with this mobility comes the need for location awareness. A prime example for this development are today’s and future transportation systems. They increasingly rely on wireless communications to exchange location and velocity information for a multitude of functions and applications. At the same time, the technological progress facilitates the widespread availability of sophisticated radio technology such as software-defined radios. The result is a variety of new attack vectors threatening the integrity of location information in mobile networks.
Although such attacks can have severe consequences in safety-critical environments such as transportation, the combination of mobility and integrity of spatial information has not received much attention in security research in the past. In this thesis we aim to fill this gap by providing adequate methods to protect the integrity of location and velocity information in the presence of mobility. Based on physical effects of mobility on wireless communications, we develop new methods to securely verify locations, sequences of locations, and velocity information provided by untrusted nodes. The results of our analyses show that mobility can in fact be exploited to provide robust security at low cost.
To further investigate the applicability of our schemes to real-world transportation systems, we have built the OpenSky Network, a sensor network which collects air traffic control communication data for scientific applications. The network uses crowdsourcing and has already achieved coverage in most parts of the world with more than 1000 sensors.
Based on the data provided by the network and measurements with commercial off-the-shelf hardware, we demonstrate the technical feasibility and security of our schemes in the air traffic scenario. Moreover, the experience and data provided by the OpenSky Network allows us to investigate the challenges for our schemes in the real-world air traffic communication environment. We show that our verification methods match all
requirements to help secure the next generation air traffic system.
Computational problems that involve dynamic data, such as physics simulations and program development environments, have been an important
subject of study in programming languages. Recent advances in self-adjusting
computation made progress towards achieving efficient incremental computation by providing algorithmic language abstractions to express computations that respond automatically to dynamic changes in their inputs. Selfadjusting programs have been shown to be efficient for a broad range of problems via an explicit programming style, where the programmer uses specific
primitives to identify, create and operate on data that can change over time.
This dissertation presents implicit self-adjusting computation, a type directed technique for translating purely functional programs into self-adjusting
programs. In this implicit approach, the programmer annotates the (toplevel) input types of the programs to be translated. Type inference finds
all other types, and a type-directed translation rewrites the source program
into an explicitly self-adjusting target program. The type system is related to
information-flow type systems and enjoys decidable type inference via constraint solving. We prove that the translation outputs well-typed self-adjusting
programs and preserves the source program’s input-output behavior, guaranteeing that translated programs respond correctly to all changes to their
data. Using a cost semantics, we also prove that the translation preserves the
asymptotic complexity of the source program.
As a second contribution, we present two techniques to facilitate the processing of large and dynamic data in self-adjusting computation. First, we
present a type system for precise dependency tracking that minimizes the
time and space for storing dependency metadata. The type system improves
the scalability of self-adjusting computation by eliminating an important assumption of prior work that can lead to recording spurious dependencies.
We present a type-directed translation algorithm that generates correct selfadjusting programs without relying on this assumption. Second, we show a
probabilistic-chunking technique to further decrease space usage by controlling the fundamental space-time tradeoff in self-adjusting computation.
We implement implicit self-adjusting computation as an extension to Standard ML with compiler and runtime support. Using the compiler, we are able
to incrementalize an interesting set of applications, including standard list
and matrix benchmarks, ray tracer, PageRank, sparse graph connectivity, and
social circle counts. Our experiments show that our compiler incrementalizes existing code with only trivial amounts of annotation, and the resulting
programs bring asymptotic improvements to large datasets from real-world
applications, leading to orders of magnitude speedups in practice.
Tables or ranked lists summarize facts about a group of entities in a concise and structured fashion. They are found in all kind of domains and easily comprehensible by humans. Some globally prominent examples of such rankings are the tallest buildings in the World, the richest people in Germany, or most powerful cars. The availability of vast amounts of tables or rankings from open domain allows different ways to explore data. Computing similarity between ranked lists, in order to find those lists where entities are presented in a similar order, carries important analytical insights. This thesis presents a novel query-driven Locality Sensitive Hashing (LSH) method, in order to efficiently find similar top-k rankings for a given input ranking. Experiments show that the proposed method provides a far better performance than inverted-index--based approaches, in particular, it is able to outperform the popular prefix-filtering method. Additionally, an LSH-based probabilistic pruning approach is proposed that optimizes the space utilization of inverted indices, while still maintaining a user-provided recall requirement for the results of the similarity search. Further, this thesis addresses the problem of automatically identifying interesting categorical attributes, in order to explore the entity-centric data by organizing them into meaningful categories. Our approach proposes novel statistical measures, beyond known concepts, like information entropy, in order to capture the distribution of data to train a classifier that can predict which categorical attribute will be perceived suitable by humans for data categorization. We further discuss how the information of useful categories can be applied in PANTHEON and PALEO, two data exploration frameworks developed in our group.
In recent years, enormous progress has been made in the field of Artificial Intelligence (AI). Especially the introduction of Deep Learning and end-to-end learning, the availability of large datasets and the necessary computational power in form of specialised hardware allowed researchers to build systems with previously unseen performance in areas such as computer vision, machine translation and machine gaming. In parallel, the Semantic Web and its Linked Data movement have published many interlinked RDF datasets, forming the world’s largest, decentralised and publicly available knowledge base.
Despite these scientific successes, all current systems are still narrow AI systems. Each of them is specialised to a specific task and cannot easily be adapted to all other human intelligence tasks, as would be necessary for Artificial General Intelligence (AGI). Furthermore, most of the currently developed systems are not able to learn by making use of freely available knowledge such as provided by the Semantic Web. Autonomous incorporation of new knowledge is however one of the pre-conditions for human-like problem solving.
This work provides a small step towards teaching machines such human-like reasoning on freely available knowledge from the Semantic Web. We investigate how human associations, one of the building blocks of our thinking, can be simulated with Linked Data. The two main results of these investigations are a ground truth dataset of semantic associations and a machine learning algorithm that is able to identify patterns for them in huge knowledge bases.
The ground truth dataset of semantic associations consists of DBpedia entities that are known to be strongly associated by humans. The dataset is published as RDF and can be used for future research.
The developed machine learning algorithm is an evolutionary algorithm that can learn SPARQL queries from a given SPARQL endpoint based on a given list of exemplary source-target entity pairs. The algorithm operates in an end-to-end learning fashion, extracting features in form of graph patterns without the need for human intervention. The learned patterns form a feature space adapted to the given list of examples and can be used to predict target candidates from the SPARQL endpoint for new source nodes. On our semantic association ground truth dataset, our evolutionary graph pattern learner reaches a Recall@10 of > 63 % and an MRR (& MAP) > 43 %, outperforming all baselines. With an achieved Recall@1 of > 34% it even reaches average human top response prediction performance. We also demonstrate how the graph pattern learner can be applied to other interesting areas without modification.
We present a study comparing the effect of real-time wearable feedback with traditional training methods for cardiopulmonary resuscitation (CPR). The aim is to ensure that the students can deliver CPR with the right compression speed and depth. On the wearable side, we test two systems: one based on a combination of visual feedback and tactile information on a smart-watch and one based on visual feedback and audio information on a Google Glass. In a trial with 50 subjects (23 trainee nurses and 27 novices,) we compare those modalities to standard human teaching that is used in nurse training. While a single traditional teaching session tends to improve only the percentage of correct depth, it has less effect on the percentage of effective CPR (depth and speed correct at the same time). By contrast, in a training session with the wearable feedback device, the average percentage of time when CPR is effective improves by up to almost 25%.
Education is the Achilles heel of successful resuscitation in cardiac arrest. Therefore, we aim to contribute to the educational efficiency by providing a novel augmented-reality (AR) guided interactive cardiopulmonary resuscitation (CPR) "trainer". For this trainer, a mixed reality smart glass, Microsoft HoloLens, and a CPR manikin covered with pressure sensors were used. To introduce the CPR procedure to a learner, an application with an intractable virtual teacher model was designed. The teaching scenario consists of the two main parts, theory and practice. In the theoretical part, the virtual teacher provides all information about the CPR procedure. Afterward, the user will be asked to perform the CPR cycles in three different stages. In the first two stages, it is aimed to gain the muscle memory with audio and optical feedback system. In the end, the performance of the participant is evaluated by the virtual teacher.
Fast Internet content delivery relies on two layers of caches on the request path. Firstly, content delivery networks (CDNs) seek to answer user requests before they traverse slow Internet paths. Secondly, aggregation caches in data centers seek to answer user requests before they traverse slow backend systems. The key challenge in managing these caches is the high variability of object sizes, request patterns, and retrieval latencies. Unfortunately, most existing literature focuses on caching with low (or no) variability in object sizes and ignores the intricacies of data center subsystems.
This thesis seeks to fill this gap with three contributions. First, we design a new caching system, called AdaptSize, that is robust under high object size variability. Second, we derive a method (called Flow-Offline Optimum or FOO) to predict the optimal cache hit ratio under variable object sizes. Third, we design a new caching system, called RobinHood, that exploits variances in retrieval latencies to deliver faster responses to user requests in data centers.
The techniques proposed in this thesis significantly improve the performance of CDN and data center caches. On two production traces from one of the world's largest CDN AdaptSize achieves 30-91% higher hit ratios than widely-used production systems, and 33-46% higher hit ratios than state-of-the-art research systems. Further, AdaptSize reduces the latency by more than 30% at the median, 90-percentile and 99-percentile.
We evaluate the accuracy of our FOO analysis technique on eight different production traces spanning four major Internet companies.
We find that FOO's error is at most 0.3%. Further, FOO reveals that the gap between online policies and OPT is much larger than previously thought: 27% on average, and up to 43% on web application traces.
We evaluate RobinHood with production traces from a major Internet company on a 50-server cluster. We find that RobinHood improves the 99-percentile latency by more than 50% over existing caching systems.
As load imbalances grow, RobinHood's latency improvement can be more than 2x. Further, we show that RobinHood is robust against server failures and adapts to automatic scaling of backend systems.
The results of this thesis demonstrate the power of guiding the design of practical caching policies using mathematical performance models and analysis. These models are general enough to find application in other areas of caching design and future challenges in Internet content delivery.
Nowadays, the increasing demand for ever more customizable products has emphasized the need for more flexible and fast-changing manufacturing systems. In this environment, simulation has become a strategic tool for the design, development, and implementation of such systems. Simulation represents a relatively low-cost and risk-free alternative for testing the impact and effectiveness of changes in different aspects of manufacturing systems.
Systems that deal with this kind of data for its use in decision making processes are known as Simulation-Based Decision Support Systems (SB-DSS). Although most SB-DSS provide a powerful variety of tools for the automatic and semi-automatic analysis of simulations, visual and interactive alternatives for the manual exploration of the results are still open to further development.
The work in this dissertation is focused on enhancing decision makers’ analysis capabilities by making simulation data more accessible through the incorporation of visualization and analysis techniques. To demonstrate how this goal can be achieved, two systems were developed. The first system, viPhos – standing for visualization of Phos: Greek for light –, is a system that supports lighting design in factory layout planning. viPhos combines simulation, analysis, and visualization tools and techniques to facilitate the global and local (overall factory or single workstations, respectively) interactive exploration and comparison of lighting design alternatives.
The second system, STRAD - standing for Spatio-Temporal Radar -, is a web-based systems that considers the spatio/attribute-temporal analysis of event data. Since decision making processes in manufacturing also involve the monitoring of the systems over time, STRAD enables the multilevel exploration of event data (e.g., simulated or historical registers of the status of machines or results of quality control processes).
A set of four case studies and one proof of concept prepared for both systems demonstrate the suitability of the visualization and analysis strategies adopted for supporting decision making processes in diverse application domains. The results of these case studies indicate that both, the systems as well as the techniques included in the systems can be generalized and extended to support the analysis of different tasks and scenarios.