Why-Query Support in Graph Databases

In the last few decades, database management systems became powerful tools for storing large amount of data and executing complex queries over them. In addition to extended functionality, novel types of databases appear like triple stores, distributed databases, etc. Graph databases implementing the property-graph model belong to this development branch and provide a new way for storing and processing data in the form of a graph with nodes representing some entities and edges describing connections between them. This consideration makes them suitable for keeping data without a rigid schema for use cases like social-network processing or data integration. In addition to a flexible storage, graph databases provide new querying possibilities in the form of path queries, detection of connected components, pattern matching, etc. However, the schema flexibility and graph queries come with additional costs. With limited knowledge about data and little experience in constructing the complex queries, users can create such ones, which deliver unexpected results. Forced to debug queries manually and overwhelmed by the amount of query constraints, users can get frustrated by using graph databases. What is really needed, is to improve usability of graph databases by providing debugging and explaining functionality for such situations. We have to assist users in the discovery of what were the reasons of unexpected results and what can be done in order to fix them. The unexpectedness of result sets can be expressed in terms of their size or content. In the first case, users have to solve the empty-answer, too-many-, or too-few-answers problems. In the second case, users care about the result content and miss some expected answers or wonder about presence of some unexpected ones. Considering the typical problems of receiving no or too many results by querying graph databases, in this thesis we focus on investigating the problems of the first group, whose solutions are usually represented by why-empty, why-so-few, and why-so-many queries. Our objective is to extend graph databases with debugging functionality in the form of why-queries for unexpected query results on the example of pattern matching queries, which are one of general graph-query types. We present a comprehensive analysis of existing debugging tools in the state-of-the-art research and identify their common properties. From them, we formulate the following features of why-queries, which we discuss in this thesis, namely: holistic support of different cardinality-based problems, explanation of unexpected results and query reformulation, comprehensive analysis of explanations, and non-intrusive user integration. To support different cardinality-based problems, we develop methods for explaining no, too few, and too many results. To cover different kinds of explanations, we present two types: subgraph- and modification-based explanations. The first type identifies the reasons of unexpectedness in terms of query subgraphs and delivers differential graphs as answers. The second one reformulates queries in such a way that they produce better results. Considering graph queries to be complex structures with multiple constraints, we investigate different ways of generating explanations starting from the most general one that considers only a query topology through coarse-grained rewriting up to fine-grained modification that allows fine changes of predicates and topology. To provide a comprehensive analysis of explanations, we propose to compare them on three levels including a syntactic description, a content, and a size of a result set. In order to deliver user-aware explanations, we discuss two models for non-intrusive user integration in the generation process. With the techniques proposed in this thesis, we are able to provide fundamentals for debugging of pattern-matching queries, which deliver no, too few, or too many results, in graph databases implementing the property-graph model

Leveraging query logs for user-centric OLAP

OLAP (On-Line Analytical Processing), the process of efficiently enabling common analytical operations on the multidimensional view of data, is a corner stone of Business Intelligence.While OLAP is now a mature, efficiently implemented technology, very little attention has been paid to the effectiveness of the analysis and the user-friendliness of this technology, often considered tedious of use.This dissertation is a contribution to developing user-centric OLAP, focusing on the use of former queries logged by an OLAP server to enhance subsequent analyses. It shows how logs of OLAP queries can be modeled, constructed, manipulated, compared, and finally leveraged for personalization and recommendation.Logs are modeled as sets of analytical sessions, sessions being modeled as sequences of OLAP queries. Three main approaches are presented for modeling queries: as unevaluated collections of fragments (e.g., group by sets, sets of selection predicates, sets of measures), as sets of references obtained by partially evaluating the query over dimensions, or as query answers. Such logs can be constructed even from sets of SQL query expressions, by translating these expressions into a multidimensional algebra, and bridging the translations to detect analytical sessions. Logs can be searched, filtered, compared, combined, modified and summarized with a language inspired by the relational algebra and parametrized by binary relations over sessions. In particular, these relations can be specialization relations or based on similarity measures tailored for OLAP queries and analytical sessions. Logs can be mined for various hidden knowledge, that, depending on the query model used, accurately represents the user behavior extracted.This knowledge includes simple preferences, navigational habits and discoveries made during former explorations,and can be it used in various query personalization or query recommendation approaches.Such approaches vary in terms of formulation effort, proactiveness, prescriptiveness and expressive power:query personalization, i.e., coping with a current query too few or too many results, can use dedicated operators for expressing preferences, or be based on query expansion;query recommendation, i.e., suggesting queries to pursue an analytical session,can be based on information extracted from the current state of the database and the query, or be purely history based, i.e., leveraging the query log.While they can be immediately integrated into a complete architecture for User-Centric Query Answering in data warehouses, the models and approaches introduced in this dissertation can also be seen as a starting point for assessing the effectiveness of analytical sessions, with the ultimate goal to enhance the overall decision making process

Keyword Search in Relational Databases: Architecture, Approaches and Considerations

Questo lavoro di tesi presenta le diverse soluzioni proposte in letteratura per applicare il paradigma keyword search alle basi di dati relazionali, e vuole delineare una architettura generale per definire e sviluppare questi sistemi. A tal proposito, le soluzioni presentate dalla comunità scientifica sono state analizzate focalizzandosi sui singoli componenti della pipeline di ricerca. Infine, si sono analizzati i processi di valutazione sperimentale di questi sistem

Processing Rank-Aware Queries in Schema-Based P2P Systems

Effiziente Anfragebearbeitung in Datenintegrationssystemen sowie in P2P-Systemen ist bereits seit einigen Jahren ein Aspekt aktueller Forschung. Konventionelle Datenintegrationssysteme bestehen aus mehreren Datenquellen mit ggf. unterschiedlichen Schemata, sind hierarchisch aufgebaut und besitzen eine zentrale Komponente: den Mediator, der ein globales Schema verwaltet. Anfragen an das System werden auf diesem globalen Schema formuliert und vom Mediator bearbeitet, indem relevante Daten von den Datenquellen transparent für den Benutzer angefragt werden. Aufbauend auf diesen Systemen entstanden schließlich Peer-Daten-Management-Systeme (PDMSs) bzw. schemabasierte P2P-Systeme. An einem PDMS teilnehmende Knoten (Peers) können einerseits als Mediatoren agieren andererseits jedoch ebenso als Datenquellen. Darüber hinaus sind diese Peers autonom und können das Netzwerk jederzeit verlassen bzw. betreten. Die potentiell riesige Datenmenge, die in einem derartigen Netzwerk verfügbar ist, führt zudem in der Regel zu sehr großen Anfrageergebnissen, die nur schwer zu bewältigen sind. Daher ist das Bestimmen einer vollständigen Ergebnismenge in vielen Fällen äußerst aufwändig oder sogar unmöglich. In diesen Fällen bietet sich die Anwendung von Top-N- und Skyline-Operatoren, ggf. in Verbindung mit Approximationstechniken, an, da diese Operatoren lediglich diejenigen Datensätze als Ergebnis ausgeben, die aufgrund nutzerdefinierter Ranking-Funktionen am relevantesten für den Benutzer sind. Da durch die Anwendung dieser Operatoren zumeist nur ein kleiner Teil des Ergebnisses tatsächlich dem Benutzer ausgegeben wird, muss nicht zwangsläufig die vollständige Ergebnismenge berechnet werden sondern nur der Teil, der tatsächlich relevant für das Endergebnis ist. Die Frage ist nun, wie man derartige Anfragen durch die Ausnutzung dieser Erkenntnis effizient in PDMSs bearbeiten kann. Die Beantwortung dieser Frage ist das Hauptanliegen dieser Dissertation. Zur Lösung dieser Problemstellung stellen wir effiziente Anfragebearbeitungsstrategien in PDMSs vor, die die charakteristischen Eigenschaften ranking-basierter Operatoren sowie Approximationstechniken ausnutzen. Peers werden dabei sowohl auf Schema- als auch auf Datenebene hinsichtlich der Relevanz ihrer Daten geprüft und dementsprechend in die Anfragebearbeitung einbezogen oder ausgeschlossen. Durch die Heterogenität der Peers werden Techniken zum Umschreiben einer Anfrage von einem Schema in ein anderes nötig. Da existierende Techniken zum Umschreiben von Anfragen zumeist nur konjunktive Anfragen betrachten, stellen wir eine Erweiterung dieser Techniken vor, die Anfragen mit ranking-basierten Anfrageoperatoren berücksichtigt. Da PDMSs dynamische Systeme sind und teilnehmende Peers jederzeit ihre Daten ändern können, betrachten wir in dieser Dissertation nicht nur wie Routing-Indexe verwendet werden, um die Relevanz eines Peers auf Datenebene zu bestimmen, sondern auch wie sie gepflegt werden können. Schließlich stellen wir SmurfPDMS (SiMUlating enviRonment For Peer Data Management Systems) vor, ein System, welches im Rahmen dieser Dissertation entwickelt wurde und alle vorgestellten Techniken implementiert.In recent years, there has been considerable research with respect to query processing in data integration and P2P systems. Conventional data integration systems consist of multiple sources with possibly different schemas, adhere to a hierarchical structure, and have a central component (mediator) that manages a global schema. Queries are formulated against this global schema and the mediator processes them by retrieving relevant data from the sources transparently to the user. Arising from these systems, eventually Peer Data Management Systems (PDMSs), or schema-based P2P systems respectively, have attracted attention. Peers participating in a PDMS can act both as a mediator and as a data source, are autonomous, and might leave or join the network at will. Due to these reasons peers often hold incomplete or erroneous data sets and mappings. The possibly huge amount of data available in such a network often results in large query result sets that are hard to manage. Due to these reasons, retrieving the complete result set is in most cases difficult or even impossible. Applying rank-aware query operators such as top-N and skyline, possibly in conjunction with approximation techniques, is a remedy to these problems as these operators select only those result records that are most relevant to the user. Being aware that in most cases only a small fraction of the complete result set is actually output to the user, retrieving the complete set before evaluating such operators is obviously inefficient. Therefore, the questions we want to answer in this dissertation are how to compute such queries in PDMSs and how to do that efficiently. We propose strategies for efficient query processing in PDMSs that exploit the characteristics of rank-aware queries and optionally apply approximation techniques. A peer's relevance is determined on two levels: on schema-level and on data-level. According to its relevance a peer is either considered for query processing or not. Because of heterogeneity queries need to be rewritten, enabling cooperation between peers that use different schemas. As existing query rewriting techniques mostly consider conjunctive queries only, we present an extension that allows for rewriting queries involving rank-aware query operators. As PDMSs are dynamic systems and peers might update their local data, this dissertation addresses not only the problem of considering such structures within a query processing strategy but also the problem of keeping them up-to-date. Finally, we provide a system-level evaluation by presenting SmurfPDMS (SiMUlating enviRonment For Peer Data Management Systems) -- a system created in the context of this dissertation implementing all presented techniques

Ranked Similarity Search of Scientific Datasets: An Information Retrieval Approach

In the past decade, the amount of scientific data collected and generated by scientists has grown dramatically. This growth has intensified an existing problem: in large archives consisting of datasets stored in many files, formats and locations, how can scientists find data relevant to their research interests? We approach this problem in a new way: by adapting Information Retrieval techniques, developed for searching text documents, into the world of (primarily numeric) scientific data. We propose an approach that uses a blend of automated and curated methods to extract metadata from large repositories of scientific data. We then perform searches over this metadata, returning results ranked by similarity to the search criteria. We present a model of this approach, and describe a specific implementation thereof performed at an ocean-observatory data archive and now running in production. Our prototype implements scanners that extract metadata from datasets that contain different kinds of environmental observations, and a search engine with a candidate similarity measure for comparing a set of search terms to the extracted metadata. We evaluate the utility of the prototype by performing two user studies; these studies show that the approach resonates with users, and that our proposed similarity measure performs well when analyzed using standard Information Retrieval evaluation methods. We performed performance tests to explore how continued archive growth will affect our goal of interactive response, developed and applied techniques that mitigate the effects of that growth, and show that the techniques are effective. Lastly, we describe some of the research needed to extend this initial work into a true Google for data

Effizienz in Cluster-Datenbanksystemen - Dynamische und Arbeitslastberücksichtigende Skalierung und Allokation

Database systems have been vital in all forms of data processing for a long time. In recent years, the amount of processed data has been growing dramatically, even in small projects. Nevertheless, database management systems tend to be static in terms of size and performance which makes scaling a difficult and expensive task. Because of performance and especially cost advantages more and more installed systems have a shared nothing cluster architecture. Due to the massive parallelism of the hardware programming paradigms from high performance computing are translated into data processing. Database research struggles to keep up with this trend. A key feature of traditional database systems is to provide transparent access to the stored data. This introduces data dependencies and increases system complexity and inter process communication. Therefore, many developers are exchanging this feature for a better scalability. However, explicitly managing the data distribution and data flow requires a deep understanding of the distributed system and reduces the possibilities for automatic and autonomic optimization. In this thesis we present an approach for database system scaling and allocation that features good scalability although it keeps the data distribution transparent. The first part of this thesis analyzes the challenges and opportunities for self-scaling database management systems in cluster environments. Scalability is a major concern of Internet based applications. Access peaks that overload the application are a financial risk. Therefore, systems are usually configured to be able to process peaks at any given moment. As a result, server systems often have a very low utilization. In distributed systems the efficiency can be increased by adapting the number of nodes to the current workload. We propose a processing model and an architecture that allows efficient self-scaling of cluster database systems. In the second part we consider different allocation approaches. To increase the efficiency we present a workload-aware, query-centric model. The approach is formalized; optimal and heuristic algorithms are presented. The algorithms optimize the data distribution for local query execution and balance the workload according to the query history. We present different query classification schemes for different forms of partitioning. The approach is evaluated for OLTP and OLAP style workloads. It is shown that variants of the approach scale well for both fields of application. The third part of the thesis considers benchmarks for large, adaptive systems. First, we present a data generator for cloud-sized applications. Due to its architecture the data generator can easily be extended and configured. A key feature is the high degree of parallelism that makes linear speedup for arbitrary numbers of nodes possible. To simulate systems with user interaction, we have analyzed a productive online e-learning management system. Based on our findings, we present a model for workload generation that considers the temporal dependency of user interaction.Datenbanksysteme sind seit langem die Grundlage für alle Arten von Informationsverarbeitung. In den letzten Jahren ist das Datenaufkommen selbst in kleinen Projekten dramatisch angestiegen. Dennoch sind viele Datenbanksysteme statisch in Bezug auf ihre Kapazität und Verarbeitungsgeschwindigkeit was die Skalierung aufwendig und teuer macht. Aufgrund der guten Geschwindigkeit und vor allem aus Kostengründen haben immer mehr Systeme eine Shared-Nothing-Architektur, bestehen also aus unabhängigen, lose gekoppelten Rechnerknoten. Da dieses Konstruktionsprinzip einen sehr hohen Grad an Parallelität aufweist, werden zunehmend Programmierparadigmen aus dem klassischen Hochleistungsrechen für die Informationsverarbeitung eingesetzt. Dieser Trend stellt die Datenbankforschung vor große Herausforderungen. Eine der grundlegenden Eigenschaften traditioneller Datenbanksysteme ist der transparente Zugriff zu den gespeicherten Daten, der es dem Nutzer erlaubt unabhängig von der internen Organisation auf die Daten zuzugreifen. Die resultierende Unabhängigkeit führt zu Abhängigkeiten in den Daten und erhöht die Komplexität der Systeme und der Kommunikation zwischen einzelnen Prozessen. Daher wird Transparenz von vielen Entwicklern für eine bessere Skalierbarkeit geopfert. Diese Entscheidung führt dazu, dass der die Datenorganisation und der Datenfluss explizit behandelt werden muss, was die Möglichkeiten für eine automatische und autonome Optimierung des Systems einschränkt. Der in dieser Arbeit vorgestellte Ansatz zur Skalierung und Allokation erhält den transparenten Zugriff und zeichnet sich dabei durch seine vollständige Automatisierbarkeit und sehr gute Skalierbarkeit aus. Im ersten Teil dieser Dissertation werden die Herausforderungen und Chancen für selbst-skalierende Datenbankmanagementsysteme behandelt, die in auf Computerclustern betrieben werden. Gute Skalierbarkeit ist eine notwendige Eigenschaft für Anwendungen, die über das Internet zugreifbar sind. Lastspitzen im Zugriff, die die Anwendung überladen stellen ein finanzielles Risiko dar. Deshalb werden Systeme so konfiguriert, dass sie eventuelle Lastspitzen zu jedem Zeitpunkt verarbeiten können. Das führt meist zu einer im Schnitt sehr geringen Auslastung der unterliegenden Systeme. Eine Möglichkeit dieser Ineffizienz entgegen zu steuern ist es die Anzahl der verwendeten Rechnerknoten an die vorliegende Last anzupassen. In dieser Dissertation werden ein Modell und eine Architektur für die Anfrageverarbeitung vorgestellt, mit denen es möglich ist Datenbanksysteme auf Clusterrechnern einfach und effizient zu skalieren. Im zweiten Teil der Arbeit werden verschieden Möglichkeiten für die Datenverteilung behandelt. Um die Effizienz zu steigern wird ein Modell verwendet, das die Lastverteilung im Anfragestrom berücksichtigt. Der Ansatz ist formalisiert und optimale und heuristische Lösungen werden präsentiert. Die vorgestellten Algorithmen optimieren die Datenverteilung für eine lokale Ausführung aller Anfragen und balancieren die Last auf den Rechnerknoten. Es werden unterschiedliche Arten der Anfrageklassifizierung vorgestellt, die zu verschiedenen Arten von Partitionierung führen. Der Ansatz wird sowohl für Onlinetransaktionsverarbeitung, als auch Onlinedatenanalyse evaluiert. Die Evaluierung zeigt, dass der Ansatz für beide Felder sehr gut skaliert. Im letzten Teil der Arbeit werden verschiedene Techniken für die Leistungsmessung von großen, adaptiven Systemen präsentiert. Zunächst wird ein Datengenerierungsansatz gezeigt, der es ermöglicht sehr große Datenmengen völlig parallel zu erzeugen. Um die Benutzerinteraktion von Onlinesystemen zu simulieren wurde ein produktives E-learningsystem analysiert. Anhand der Analyse wurde ein Modell für die Generierung von Arbeitslasten erstellt, das die zeitlichen Abhängigkeiten von Benutzerinteraktion berücksichtigt

Optimization-based User Group Management : Discovery, Analysis, Recommendation

User data is becoming increasingly available in multiple domains ranging from phone usage traces to data on the social Web. User data is a special type of data that is described by user demographics (e.g., age, gender, occupation, etc.) and user activities (e.g., rating, voting, watching a movie, etc.) The analysis of user data is appealing to scientists who work on population studies, online marketing, recommendations, and large-scale data analytics. However, analysis tools for user data is still lacking.In this thesis, we believe there exists a unique opportunity to analyze user data in the form of user groups. This is in contrast with individual user analysis and also statistical analysis on the whole population. A group is defined as set of users whose members have either common demographics or common activities. Group-level analysis reduces the amount of sparsity and noise in data and leads to new insights. In this thesis, we propose a user group management framework consisting of following components: user group discovery, analysis and recommendation.The very first step in our framework is group discovery, i.e., given raw user data, obtain user groups by optimizing one or more quality dimensions. The second component (i.e., analysis) is necessary to tackle the problem of information overload: the output of a user group discovery step often contains millions of user groups. It is a tedious task for an analyst to skim over all produced groups. Thus we need analysis tools to provide valuable insights in this huge space of user groups. The final question in the framework is how to use the found groups. In this thesis, we investigate one of these applications, i.e., user group recommendation, by considering affinities between group members.All our contributions of the proposed framework are evaluated using an extensive set of experiments both for quality and performance.Les donn ́ees utilisateurs sont devenue de plus en plus disponibles dans plusieurs do- maines tels que les traces d'usage des smartphones et le Web social. Les donn ́ees util- isateurs, sont un type particulier de donn ́ees qui sont d ́ecrites par des informations socio-d ́emographiques (ex., ˆage, sexe, m ́etier, etc.) et leurs activit ́es (ex., donner un avis sur un restaurant, voter, critiquer un film, etc.). L'analyse des donn ́ees utilisa- teurs int ́eresse beaucoup les scientifiques qui travaillent sur les ́etudes de la population, le marketing en-ligne, les recommandations et l'analyse des donn ́ees `a grande ́echelle. Cependant, les outils d'analyse des donn ́ees utilisateurs sont encore tr`es limit ́es.Dans cette th`ese, nous exploitons cette opportunit ́e et proposons d'analyser les donn ́ees utilisateurs en formant des groupes d'utilisateurs. Cela diff`ere de l'analyse des util- isateurs individuels et aussi des analyses statistiques sur une population enti`ere. Un groupe utilisateur est d ́efini par un ensemble des utilisateurs dont les membres parta- gent des donn ́ees socio-d ́emographiques et ont des activit ́es en commun. L'analyse au niveau d'un groupe a pour objectif de mieux g ́erer les donn ́ees creuses et le bruit dans les donn ́ees. Dans cette th`ese, nous proposons un cadre de gestion de groupes d'utilisateurs qui contient les composantes suivantes: d ́ecouverte de groupes, analyse de groupes, et recommandation aux groupes.La premi`ere composante concerne la d ́ecouverte des groupes d'utilisateurs, c.- `a-d., compte tenu des donn ́ees utilisateurs brutes, obtenir les groupes d'utilisateurs en op- timisantuneouplusieursdimensionsdequalit ́e. Ledeuxi`emecomposant(c.-`a-d., l'analyse) est n ́ecessaire pour aborder le probl`eme de la surcharge de l'information: le r ́esultat d'une ́etape d ́ecouverte des groupes d'utilisateurs peut contenir des millions de groupes. C'est une tache fastidieuse pour un analyste `a ́ecumer tous les groupes trouv ́es. Nous proposons une approche interactive pour faciliter cette analyse. La question finale est comment utiliser les groupes trouv ́es. Dans cette th`ese, nous ́etudions une applica- tion particuli`ere qui est la recommandation aux groupes d'utilisateurs, en consid ́erant les affinit ́es entre les membres du groupe et son ́evolution dans le temps.Toutes nos contributions sont ́evalu ́ees au travers d'un grand nombre d'exp ́erimentations `a la fois pour tester la qualit ́e et la performance (le temps de r ́eponse)