Mining spatio-temporal reachable regions aims to find a set of road segments from massive trajectory data, that are reachable from a user-specified location and within a given temporal period. Accurately extracting such spatio-temporal reachable area is vital in many urban applications, e.g., (i) location-based recommendation, (ii) location-based advertising, and (iii) business coverage analysis. The traditional approach of answering such queries essentially performs a distance-based range query over the given road network, which have two main drawbacks: (i) it only works with the physical travel distances, where the users usually care more about dynamic traveling time, and (ii) it gives the same result regardless of the querying time, where the reachable area could vary significantly with different traffic conditions. Motivated by these observations, in this thesis, we propose a data- driven approach to formulate the problem as mining actual reachable region based on real historical trajectory dataset. The main challenge in our approach is the system efficiency, as verifying the reachability over the massive trajectories involves huge amount of disk I/Os. In this thesis, we develop two indexing structures: 1) spatio-temporal index (ST-Index) and 2) connection index (Con-Index) to reduce redundant trajectory data access operations. We also propose a novel query processing algorithm with: 1) maximum bounding region search, which directly extracts a small searching region from the index structure and 2) trace back search, which refines the search results from the previous step to find the final query result. Moreover, our system can also efficiently answer the spatio-temporal reachability query with multiple query locations by skipping the overlapped area search. We evaluate our system extensively using a large-scale real taxi trajectory data in Shenzhen, China, where results demonstrate that the proposed algorithms can reduce 50%-90% running time over baseline algorithms.

Graphen sind in vielen Bereichen des Lebens zu finden, wobei wir speziell an Graphen in der Biologie interessiert sind. Knoten in solchen Graphen sind chemische Komponenten, Enzyme, Reaktionen oder Interaktionen, die durch Kanten miteinander verbunden sind. Eine effiziente Ausführung von Graphanfragen ist eine Herausforderung. In dieser Arbeit präsentieren wir GRIcano, ein System, das die effiziente Ausführung von Graphanfragen erlaubt. Wir nehmen an, dass Graphen in relationalen Datenbankmanagementsystemen (RDBMS) gespeichert sind. Als Graphanfragesprache schlagen wir eine erweiterte Version der Pathway Query Language (PQL) vor. Der Hauptbestandteil von GRIcano ist ein kostenbasierter Anfrageoptimierer. Diese Arbeit enthält Beiträge zu allen drei benötigten Komponenten des Optimierers, der relationalen Algebra, Implementierungen und Kostenmodellen. Die Operatoren der relationalen Algebra sind nicht ausreichend, um Graphanfragen auszudrücken. Daher stellen wir zuerst neue Operatoren vor. Wir schlagen den Erreichbarkeits-, Distanz-, Pfadlängen- und Pfadoperator vor. Zusätzlich geben wir Regeln für die Umformung von Ausdrücken an. Des Weiteren präsentieren wir Implementierungen für jeden vorgeschlagenen Operator. Der Hauptbeitrag ist GRIPP, eine Indexstruktur, die die effiziente Ausführung von Erreichbarkeitsanfragen auf sehr großen Graphen erlaubt. Wir zeigen, wie GRIPP und die rekursive Anfragestrategie genutzt werden können, um Implementierungen für alle Operatoren bereitzustellen. Die dritte Komponente von GRIcano ist das Kostenmodell, das Kardinalitätsabschätzungen der Operatoren und Kostenfunktionen für die Implementierungen benötigt. Basierend auf umfangreichen Experimenten schlagen wir in dieser Arbeit Funktionen dafür vor. Der neue Ansatz unserer Kostenmodelle ist, dass die Funktionen nur Kennzahlen der Graphen verwenden. Abschließend zeigen wir die Wirkungsweise von GRIcano durch Beispielanfragen auf echten biologischen Graphen. / Graphs occur in many areas of life. We are interested in graphs in biology, where nodes are chemical compounds, enzymes, reactions, or interactions that are connected by edges. Efficiently querying these graphs is a challenging task. In this thesis we present GRIcano, a system that efficiently executes graph queries. For GRIcano we assume that graphs are stored and queried using relational database management systems (RDBMS). We propose an extended version of the Pathway Query Language PQL to express graph queries. The core of GRIcano is a cost-based query optimizer. This thesis makes contributions to all three required components of the optimizer, the relational algebra, implementations, and cost model. Relational algebra operators alone are not sufficient to express graph queries. Thus, we first present new operators to rewrite PQL queries to algebra expressions. We propose the reachability, distance, path length, and path operator. In addition, we provide rewrite rules for the newly proposed operators in combination with standard relational algebra operators. Secondly, we present implementations for each proposed operator. The main contribution is GRIPP, an index structure that allows us to answer reachability queries on very large graphs. GRIPP has advantages over other existing index structures, which we review in this work. In addition, we show how to employ GRIPP and the recursive query strategy as implementation for all four proposed operators. The third component of GRIcano is the cost model, which requires cardinality estimates for operators and cost functions for implementations. Based on extensive experimental evaluation of our proposed algorithms we present functions to estimate the cardinality of operators and the cost of executing a query. The novelty of our approach is that these functions only use key figures of the graph. We finally present the effectiveness of GRIcano using exemplary graph queries on real biological networks.