Core Decomposition in Multilayer Networks: Theory, Algorithms, and Applications

Multilayer networks are a powerful paradigm to model complex systems, where multiple relations occur between the same entities. Despite the keen interest in a variety of tasks, algorithms, and analyses in this type of network, the problem of extracting dense subgraphs has remained largely unexplored so far. In this work we study the problem of core decomposition of a multilayer network. The multilayer context is much challenging as no total order exists among multilayer cores; rather, they form a lattice whose size is exponential in the number of layers. In this setting we devise three algorithms which differ in the way they visit the core lattice and in their pruning techniques. We then move a step forward and study the problem of extracting the inner-most (also known as maximal) cores, i.e., the cores that are not dominated by any other core in terms of their core index in all the layers. Inner-most cores are typically orders of magnitude less than all the cores. Motivated by this, we devise an algorithm that effectively exploits the maximality property and extracts inner-most cores directly, without first computing a complete decomposition. Finally, we showcase the multilayer core-decomposition tool in a variety of scenarios and problems. We start by considering the problem of densest-subgraph extraction in multilayer networks. We introduce a definition of multilayer densest subgraph that trades-off between high density and number of layers in which the high density holds, and exploit multilayer core decomposition to approximate this problem with quality guarantees. As further applications, we show how to utilize multilayer core decomposition to speed-up the extraction of frequent cross-graph quasi-cliques and to generalize the community-search problem to the multilayer setting

Similarity search and mining in uncertain spatial and spatio-temporal databases

Both the current trends in technology such as smart phones, general mobile devices, stationary sensors and satellites as well as a new user mentality of utilizing this technology to voluntarily share information produce a huge flood of geo-spatial and geo-spatio-temporal data. This data flood provides a tremendous potential of discovering new and possibly useful knowledge. In addition to the fact that measurements are imprecise, due to the physical limitation of the devices, some form of interpolation is needed in-between discrete time instances. From a complementary perspective - to reduce the communication and bandwidth utilization, along with the storage requirements, often the data is subjected to a reduction, thereby eliminating some of the known/recorded values. These issues introduce the notion of uncertainty in the context of spatio-temporal data management - an aspect raising an imminent need for scalable and flexible data management. The main scope of this thesis is to develop effective and efficient techniques for similarity search and data mining in uncertain spatial and spatio-temporal data. In a plethora of research fields and industrial applications, these techniques can substantially improve decision making, minimize risk and unearth valuable insights that would otherwise remain hidden. The challenge of effectiveness in uncertain data is to correctly determine the set of possible results, each associated with the correct probability of being a result, in order to give a user a confidence about the returned results. The contrary challenge of efficiency, is to compute these result and corresponding probabilities in an efficient manner, allowing for reasonable querying and mining times, even for large uncertain databases. The paradigm used to master both challenges, is to identify a small set of equivalent classes of possible worlds, such that members of the same class can be treated as equivalent in the context of a given query predicate or data mining task. In the scope of this work, this paradigm will be formally defined, and applied to the most prominent classes of spatial queries on uncertain data, including range queries, k-nearest neighbor queries, ranking queries and reverse k-nearest neighbor queries. For this purpose, new spatial and probabilistic pruning approaches are developed to further speed up query processing. Furthermore, the proposed paradigm allows to develop the first efficient solution for the problem of frequent co-location mining on uncertain data. Special emphasis is taken on the temporal aspect of applications using modern data collection technologies. While the aforementioned techniques work well for single points of time, the prediction of query results over time remains a challenge. This thesis fills this gap by modeling an uncertain spatio-temporal object as a stochastic process, and by applying the above paradigm to efficiently query, index and mine historical spatio-temporal data.Moderne Technologien, z.B. Sattelitentechnologie und Technologie in Smart Phones, erzeugen eine Flut räumlicher Geo-Daten. Zudem ist in der Gesellschaft ein Trend zu beobachten diese erzeugten Daten freiwillig auf öffentlich zugänglichen Plattformen zur Verfügung zu stellen. Diese Datenflut hat immenses Potential, um neues und nützliches Wissen zu entdecken. Diese Daten sind jedoch grundsätzlich unsichere räumliche Daten. Die Unsicherheit ergibt sich aus mehreren Aspekten. Zum einen kommt es bei Messungen grundsätzlich zu Messungenauigkeiten, zum anderen ist zwischen diskreten Messzeitpunkten eine Interpolation nötig, die zusätzliche Unsicherheit erzeugt. Auerdem werden die Daten oft absichtlich reduziert, um Speicherplatz und Transfervolumen einzusparen, wodurch weitere Information verloren geht. Diese Unsicherheit schafft einen sofortigen Bedarf für skalierbare und flexible Methoden zur Verwaltung und Auswertung solcher Daten. Im Rahmen dieser Arbeit sollen effektive und effiziente Techniken zur Ähnlichkeitssuche und zum Data Mining bei unsicheren räumlichen und unsicheren räumlich-zeitlichen Daten erarbeitet werden. Diese Techniken liefern wertvolles Wissen, das auf verschiedenen Forschungsgebieten, als auch bei industriellen Anwendungen zur Entscheidungsfindung genutzt werden kann. Bei der Entwicklung dieser Techniken gibt es zwei Herausforderungen. Einerseits müssen die entwickelten Techniken effektiv sein, um korrekte Ergebnisse und Wahrscheinlichkeiten dieser Ergebnisse zurückzugeben. Andererseits müssen die entwickelten Techniken effizient sein, um auch in sehr großen Datenbanken Ergebnisse in annehmbarer Zeit zu liefern. Die Dissertation stellt ein neues Paradigma vor, das beide Herausforderungen meistert. Dieses Paradigma identifiziert mögliche Datenbankwelten, die bezüglich des gegebenen Anfrageprädikats äquivalent sind. Es wird formal definiert und auf die relevantesten räumlichen Anfragetypen angewendet, um effiziente Lösungen zu entwickeln. Dazu gehören Bereichanfragen, k-Nächste-Nachbarnanfragen, Rankinganfragen und Reverse k-Nächste-Nachbarnanfragen. Räumliche und probabilistische Pruningkriterien werden entwickelt, um insignifikante Ergebnisse früh auszuschlieen. Zudem wird die erste effiziente Lösung für das Problem des "Spatial Co-location Minings" auf unsicheren Daten präsentiert. Ein besonderer Schwerpunkt dieser Arbeit liegt auf dem temporalen Aspekt moderner Geo-Daten. Während obig genannte Techniken dieser Arbeit für einzelne Zeitpunkt sehr gut funktionieren, ist die effektive und effiziente Verwaltung von unsicheren räumlich zeitlichen Daten immer noch ein weitestgehend ungelöstes Problem. Diese Dissertation löst dieses Problem, indem unsichere räumlich-zeitliche Daten durch stochastische Prozesse modeliert werden. Auf diese stochastischen Prozesse lässt sich das oben genannte Paradigma anwenden, um unsichere räumlich-zeitliche Daten effizient anzufragen, zu indexieren, und zu minen

Sistemas granulares evolutivos

Orientador: Fernando Antonio Campos GomideTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Recentemente tem-se observado um crescente interesse em abordagens de modelagem computacional para lidar com fluxos de dados do mundo real. Métodos e algoritmos têm sido propostos para obtenção de conhecimento a partir de conjuntos de dados muito grandes e, a princípio, sem valor aparente. Este trabalho apresenta uma plataforma computacional para modelagem granular evolutiva de fluxos de dados incertos. Sistemas granulares evolutivos abrangem uma variedade de abordagens para modelagem on-line inspiradas na forma com que os humanos lidam com a complexidade. Esses sistemas exploram o fluxo de informação em ambiente dinâmico e extrai disso modelos que podem ser linguisticamente entendidos. Particularmente, a granulação da informação é uma técnica natural para dispensar atenção a detalhes desnecessários e enfatizar transparência, interpretabilidade e escalabilidade de sistemas de informação. Dados incertos (granulares) surgem a partir de percepções ou descrições imprecisas do valor de uma variável. De maneira geral, vários fatores podem afetar a escolha da representação dos dados tal que o objeto representativo reflita o significado do conceito que ele está sendo usado para representar. Neste trabalho são considerados dados numéricos, intervalares e fuzzy; e modelos intervalares, fuzzy e neuro-fuzzy. A aprendizagem de sistemas granulares é baseada em algoritmos incrementais que constroem a estrutura do modelo sem conhecimento anterior sobre o processo e adapta os parâmetros do modelo sempre que necessário. Este paradigma de aprendizagem é particularmente importante uma vez que ele evita a reconstrução e o retreinamento do modelo quando o ambiente muda. Exemplos de aplicação em classificação, aproximação de função, predição de séries temporais e controle usando dados sintéticos e reais ilustram a utilidade das abordagens de modelagem granular propostas. O comportamento de fluxos de dados não-estacionários com mudanças graduais e abruptas de regime é também analisado dentro do paradigma de computação granular evolutiva. Realçamos o papel da computação intervalar, fuzzy e neuro-fuzzy em processar dados incertos e prover soluções aproximadas de alta qualidade e sumário de regras de conjuntos de dados de entrada e saída. As abordagens e o paradigma introduzidos constituem uma extensão natural de sistemas inteligentes evolutivos para processamento de dados numéricos a sistemas granulares evolutivos para processamento de dados granularesAbstract: In recent years there has been increasing interest in computational modeling approaches to deal with real-world data streams. Methods and algorithms have been proposed to uncover meaningful knowledge from very large (often unbounded) data sets in principle with no apparent value. This thesis introduces a framework for evolving granular modeling of uncertain data streams. Evolving granular systems comprise an array of online modeling approaches inspired by the way in which humans deal with complexity. These systems explore the information flow in dynamic environments and derive from it models that can be linguistically understood. Particularly, information granulation is a natural technique to dispense unnecessary details and emphasize transparency, interpretability and scalability of information systems. Uncertain (granular) data arise from imprecise perception or description of the value of a variable. Broadly stated, various factors can affect one's choice of data representation such that the representing object conveys the meaning of the concept it is being used to represent. Of particular concern to this work are numerical, interval, and fuzzy types of granular data; and interval, fuzzy, and neurofuzzy modeling frameworks. Learning in evolving granular systems is based on incremental algorithms that build model structure from scratch on a per-sample basis and adapt model parameters whenever necessary. This learning paradigm is meaningful once it avoids redesigning and retraining models all along if the system changes. Application examples in classification, function approximation, time-series prediction and control using real and synthetic data illustrate the usefulness of the granular approaches and framework proposed. The behavior of nonstationary data streams with gradual and abrupt regime shifts is also analyzed in the realm of evolving granular computing. We shed light upon the role of interval, fuzzy, and neurofuzzy computing in processing uncertain data and providing high-quality approximate solutions and rule summary of input-output data sets. The approaches and framework introduced constitute a natural extension of evolving intelligent systems over numeric data streams to evolving granular systems over granular data streamsDoutoradoAutomaçãoDoutor em Engenharia Elétric

Doctor of Philosophy

dissertationIn the era of big data, many applications generate continuous online data from distributed locations, scattering devices, etc. Examples include data from social media, financial services, and sensor networks, etc. Meanwhile, large volumes of data can be archived or stored offline in distributed locations for further data analysis. Challenges from data uncertainty, large-scale data size, and distributed data sources motivate us to revisit several classic problems for both online and offline data explorations. The problem of continuous threshold monitoring for distributed data is commonly encountered in many real-world applications. We study this problem for distributed probabilistic data. We show how to prune expensive threshold queries using various tail bounds and combine tail-bound techniques with adaptive algorithms for monitoring distributed deterministic data. We also show how to approximate threshold queries based on sampling techniques. Threshold monitoring problems can only tell a monitoring function is above or below a threshold constraint but not how far away from it. This motivates us to study the problem of continuous tracking functions over distributed data. We first investigate the tracking problem on a chain topology. Then we show how to solve tracking problems on a distributed setting using solutions for the chain model. We studied online tracking of the max function on ""broom"" tree and general tree topologies in this work. Finally, we examine building scalable histograms for distributed probabilistic data. We show how to build approximate histograms based on a partition-and-merge principle on a centralized machine. Then, we show how to extend our solutions to distributed and parallel settings to further mitigate scalability bottlenecks and deal with distributed data

Computing theoretically-sound upper bounds to expected support for frequent pattern mining problems over uncertain big data

Frequent pattern mining aims to discover implicit, previously unknown, and potentially useful knowledge in the form of sets of frequently co-occurring items, events, or objects. To mine frequent patterns from probabilistic datasets of uncertain data, where each item in a transaction is usually associated with an existential probability expressing the likelihood of its presence in that transaction, the UF-growth algorithm captures important information about uncertain data in a UF-tree structure so that expected support can be computed for each pattern. A pattern is considered frequent if its expected support meets or exceeds the user-specified threshold. However, a challenge is that the UF-tree can be large. To handle this challenge, several algorithms use smaller trees such that upper bounds to expected support can be computed. In this paper, we examine these upper bounds, and determine which ones provide tighter upper bounds to expected support for frequent pattern mining of uncertain big data

Socio-Cognitive and Affective Computing

Social cognition focuses on how people process, store, and apply information about other people and social situations. It focuses on the role that cognitive processes play in social interactions. On the other hand, the term cognitive computing is generally used to refer to new hardware and/or software that mimics the functioning of the human brain and helps to improve human decision-making. In this sense, it is a type of computing with the goal of discovering more accurate models of how the human brain/mind senses, reasons, and responds to stimuli. Socio-Cognitive Computing should be understood as a set of theoretical interdisciplinary frameworks, methodologies, methods and hardware/software tools to model how the human brain mediates social interactions. In addition, Affective Computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects, a fundamental aspect of socio-cognitive neuroscience. It is an interdisciplinary field spanning computer science, electrical engineering, psychology, and cognitive science. Physiological Computing is a category of technology in which electrophysiological data recorded directly from human activity are used to interface with a computing device. This technology becomes even more relevant when computing can be integrated pervasively in everyday life environments. Thus, Socio-Cognitive and Affective Computing systems should be able to adapt their behavior according to the Physiological Computing paradigm. This book integrates proposals from researchers who use signals from the brain and/or body to infer people's intentions and psychological state in smart computing systems. The design of this kind of systems combines knowledge and methods of ubiquitous and pervasive computing, as well as physiological data measurement and processing, with those of socio-cognitive and affective computing

Data science for buildings, a multi-scale approach bridging occupants to smart-city energy planning

In a context of global carbon emission reduction goals, buildings have been identified to detain valuable energy-saving abilities. With the exponential increase of smart, connected building automation systems, massive amounts of data are now accessible for analysis. These coupled with powerful data science methods and machine learning algorithms present a unique opportunity to identify untapped energy-saving potentials from field information, and effectively turn buildings into active assets of the built energy infrastructure.However, the diversity of building occupants, infrastructures, and the disparities in collected information has produced disjointed scales of analytics that make it tedious for approaches to scale and generalize over the building stock.This coupled with the lack of standards in the sector has hindered the broader adoption of data science practices in the field, and engendered the following questioning:How can data science facilitate the scaling of approaches and bridge disconnected spatiotemporal scales of the built environment to deliver enhanced energy-saving strategies?This thesis focuses on addressing this interrogation by investigating data-driven, scalable, interpretable, and multi-scale approaches across varying types of analytical classes. The work particularly explores descriptive, predictive, and prescriptive analytics to connect occupants, buildings, and urban energy planning together for improved energy performances.First, a novel multi-dimensional data-mining framework is developed, producing distinct dimensional outlines supporting systematic methodological approaches and refined knowledge discovery. Second, an automated building heat dynamics identification method is put forward, supporting large-scale thermal performance examination of buildings in a non-intrusive manner. The method produced 64\% of good quality model fits, against 14\% close, and 22\% poor ones out of 225 Dutch residential buildings. %, which were open-sourced in the interest of developing benchmarks. Third, a pioneering hierarchical forecasting method was designed, bridging individual and aggregated building load predictions in a coherent, data-efficient fashion. The approach was evaluated over hierarchies of 37, 140, and 383 nodal elements and showcased improved accuracy and coherency performances against disjointed prediction systems.Finally, building occupants and urban energy planning strategies are investigated under the prism of uncertainty. In a neighborhood of 41 Dutch residential buildings, occupants were determined to significantly impact optimal energy community designs in the context of weather and economic uncertainties.Overall, the thesis demonstrated the added value of multi-scale approaches in all analytical classes while fostering best data-science practices in the sector from benchmarks and open-source implementations