Scalable big data systems: Architectures and optimizations

Big data analytics has become not just a popular buzzword but also a strategic direction in information technology for many enterprises and government organizations. Even though many new computing and storage systems have been developed for big data analytics, scalable big data processing has become more and more challenging as a result of the huge and rapidly growing size of real-world data. Dedicated to the development of architectures and optimization techniques for scaling big data processing systems, especially in the era of cloud computing, this dissertation makes three unique contributions. First, it introduces a suite of graph partitioning algorithms that can run much faster than existing data distribution methods and inherently scale to the growth of big data. The main idea of these approaches is to partition a big graph by preserving the core computational data structure as much as possible to maximize intra-server computation and minimize inter-server communication. In addition, it proposes a distributed iterative graph computation framework that effectively utilizes secondary storage to maximize access locality and speed up distributed iterative graph computations. The framework not only considerably reduces memory requirements for iterative graph algorithms but also significantly improves the performance of iterative graph computations. Last but not the least, it establishes a suite of optimization techniques for scalable spatial data processing along with three orthogonal dimensions: (i) scalable processing of spatial alarms for mobile users traveling on road networks, (ii) scalable location tagging for improving the quality of Twitter data analytics and prediction accuracy, and (iii) lightweight spatial indexing for enhancing the performance of big spatial data queries.Ph.D

Processing SPARQL Queries Over Distributed RDF Graphs

We propose techniques for processing SPARQL queries over a large RDF graph in a distributed environment. We adopt a "partial evaluation and assembly" framework. Answering a SPARQL query Q is equivalent to finding subgraph matches of the query graph Q over RDF graph G. Based on properties of subgraph matching over a distributed graph, we introduce local partial match as partial answers in each fragment of RDF graph G. For assembly, we propose two methods: centralized and distributed assembly. We analyze our algorithms from both theoretically and experimentally. Extensive experiments over both real and benchmark RDF repositories of billions of triples confirm that our method is superior to the state-of-the-art methods in both the system's performance and scalability.Comment: 30 page

Efficient processing of large-scale spatio-temporal data

Millionen Geräte, wie z.B. Mobiltelefone, Autos und Umweltsensoren senden ihre Positionen zusammen mit einem Zeitstempel und weiteren Nutzdaten an einen Server zu verschiedenen Analysezwecken. Die Positionsinformationen und übertragenen Ereignisinformationen werden als Punkte oder Polygone dargestellt. Eine weitere Art räumlicher Daten sind Rasterdaten, die zum Beispiel von Kameras und Sensoren produziert werden. Diese großen räumlich-zeitlichen Datenmengen können nur auf skalierbaren Plattformen wie Hadoop und Apache Spark verarbeitet werden, die jedoch z.B. die Nachbarschaftsinformation nicht ausnutzen können - was die Ausführung bestimmter Anfragen praktisch unmöglich macht. Die wiederholten Ausführungen der Analyseprogramme während ihrer Entwicklung und durch verschiedene Nutzer resultieren in langen Ausführungszeiten und hohen Kosten für gemietete Ressourcen, die durch die Wiederverwendung von Zwischenergebnissen reduziert werden können. Diese Arbeit beschäftigt sich mit den beiden oben beschriebenen Herausforderungen. Wir präsentieren zunächst das STARK Framework für die Verarbeitung räumlich-zeitlicher Vektor- und Rasterdaten in Apache Spark. Wir identifizieren verschiedene Algorithmen für Operatoren und analysieren, wie diese von den Eigenschaften der zugrundeliegenden Plattform profitieren können. Weiterhin wird untersucht, wie Indexe in der verteilten und parallelen Umgebung realisiert werden können. Außerdem vergleichen wir Partitionierungsmethoden, die unterschiedlich gut mit ungleichmäßiger Datenverteilung und der Größe der Datenmenge umgehen können und präsentieren einen Ansatz um die auf Operatorebene zu verarbeitende Datenmenge frühzeitig zu reduzieren. Um die Ausführungszeit von Programmen zu verkürzen, stellen wir einen Ansatz zur transparenten Materialisierung von Zwischenergebnissen vor. Dieser Ansatz benutzt ein Entscheidungsmodell, welches auf den tatsächlichen Operatorkosten basiert. In der Evaluierung vergleichen wir die verschiedenen Implementierungs- sowie Konfigurationsmöglichkeiten in STARK und identifizieren Szenarien wann Partitionierung und Indexierung eingesetzt werden sollten. Außerdem vergleichen wir STARK mit verwandten Systemen. Im zweiten Teil der Evaluierung zeigen wir, dass die transparente Wiederverwendung der materialisierten Zwischenergebnisse die Ausführungszeit der Programme signifikant verringern kann.Millions of location-aware devices, such as mobile phones, cars, and environmental sensors constantly report their positions often in combination with a timestamp to a server for different kinds of analyses. While the location information of the devices and reported events is represented as points and polygons, raster data is another type of spatial data, which is for example produced by cameras and sensors. This Big spatio-temporal Data needs to be processed on scalable platforms, such as Hadoop and Apache Spark, which, however, are unaware of, e.g., spatial neighborhood, what makes them practically impossible to use for this kind of data. The repeated executions of the programs during development and by different users result in long execution times and potentially high costs in rented clusters, which can be reduced by reusing commonly computed intermediate results. Within this thesis, we tackle the two challenges described above. First, we present the STARK framework for processing spatio-temporal vector and raster data on the Apache Spark stack. For operators, we identify several possible algorithms and study how they can benefit from the underlying platform's properties. We further investigate how indexes can be realized in the distributed and parallel architecture of Big Data processing engines and compare methods for data partitioning, which perform differently well with respect to data skew and data set size. Furthermore, an approach to reduce the amount of data to process at operator level is presented. In order to reduce the execution times, we introduce an approach to transparently recycle intermediate results of dataflow programs, based on operator costs. To compute the costs, we instrument the programs with profiling code to gather the execution time and result size of the operators. In the evaluation, we first compare the various implementation and configuration possibilities in STARK and identify scenarios when and how partitioning and indexing should be applied. We further compare STARK to related systems and show that we can achieve significantly better execution times, not only when exploiting existing partitioning information. In the second part of the evaluation, we show that with the transparent cost-based materialization and recycling of intermediate results, the execution times of programs can be reduced significantly

GRAPE: Parallel Graph Query Engine

The need for graph computations is evident in a multitude of use cases. To support computations on large-scale graphs, several parallel systems have been developed. However, existing graph systems require users to recast algorithms into new models, which makes parallel graph computations as a privilege to experienced users only. Moreover, real world applications often require much more complex graph processing workflows than previously evaluated. In response to these challenges, the thesis presents GRAPE, a distributed graph computation system, shipped with various applications for social network analysis, social media marketing and functional dependencies on graphs. Firstly, the thesis presents the foundation of GRAPE. The principled approach of GRAPE is based on partial evaluation and incremental computation. Sequential graph algorithms can be plugged into GRAPE with minor changes, and get parallelized as a whole. The termination and correctness are guaranteed under a monotonic condition. Secondly, as an application on GRAPE, the thesis proposes graph-pattern association rules (GPARs) for social media marketing. GPARs help users discover regularities between entities in social graphs and identify potential customers by exploring social influence. The thesis studies the problem of discovering top-k diversified GPARs and the problem of identifying potential customers with GPARs. Although both are NP- hard, parallel scalable algorithms on GRAPE are developed, which guarantee a polynomial speedup over sequential algorithms with the increase of processors. Thirdly, the thesis proposes quantified graph patterns (QGPs), an extension of graph patterns by supporting simple counting quantifiers on edges. QGPs naturally express universal and existential quantification, numeric and ratio aggregates, as well as negation. The thesis proves that the matching problem of QGPs remains NP-complete in the absence of negation, and is DP-complete for general QGPs. In addition, the thesis introduces quantified graph association rules defined with QGPs, to identify potential customers in social media marketing. Finally, to address the issue of data consistency, the thesis proposes a class of functional dependencies for graphs, referred to as GFDs. GFDs capture both attribute-value dependencies and topological structures of entities. The satisfiability and implication problems for GFDs are studied and proved to be coNP-complete and NP-complete, respectively. The thesis also proves that the validation problem for GFDs is coNP- complete. The parallel algorithms developed on GRAPE verify that GFDs provide an effective approach to detecting inconsistencies in knowledge and social graphs

맵리듀스에서의 병렬 조인을 위한 다차원 범위 분할 기법

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2014. 8. 이상구.Joins are fundamental operations for many data analysis tasks, but are not directly supported by the MapReduce framework. This is because 1) the framework is basically designed to process a single input data set, and 2) MapReduce's key-equality based data grouping method makes it difficult to support complex join conditions. As a result, a large number of MapReduce-based join algorithms have been proposed. As in traditional shared-nothing systems, one of the major issues in join algorithms using MapReduce is handling of data skew. We propose a new skew handling method, called Multi-Dimensional Range Partitioning (MDRP), and show that the proposed method outperforms traditional skew handling methods: range-based and randomized methods. Specifically, the proposed method has the following advantages: 1) Compared to the range-based method, it considers the number of output tuples at each machine, which leads better handling of join product skew. 2) Compared with the randomized method, it exploits given join conditions before the actual join begins, so that unnecessary input duplication can be reduced. The MDRP method can be used to support advanced join operations such as theta-joins and multi-way joins. With extensive experiments using real and synthetic data sets, we evaluate the effectiveness of the proposed algorithm.Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.3 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 II. Backgrounds and RelatedWork . . . . . . . . . . . . . . . . 8 2.1 MapReduce . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2 Join Algorithms in MapReduce . . . . . . . . . . . . . . . . 11 2.2.1 Two-Way Join Algorithms . . . . . . . . . . . . . . 11 2.2.2 Multi-Way Join Algorithms . . . . . . . . . . . . . 17 2.3 Data Skew in Join Algorithms . . . . . . . . . . . . . . . . 18 2.4 Skew Handling Approaches in MapReduce . . . . . . . . . 22 2.4.1 Hash-Based Approach . . . . . . . . . . . . . . . . 22 2.4.2 Range-Based Approach . . . . . . . . . . . . . . . 24 2.4.3 Randomized Approach . . . . . . . . . . . . . . . . 26 III. Our Approach . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.1 Multi-Dimensional Range Partitioning . . . . . . . . . . . . 29 3.1.1 Creation of a Partitioning Matrix . . . . . . . . . . . 29 3.1.2 Identifying and Chopping of Heavy Cells . . . . . . 31 3.1.3 Assigning Cells to Reducers . . . . . . . . . . . . . 33 3.1.4 Join Processing using the Partitioning Matrix . . . . 35 3.2 Theoretical Analysis . . . . . . . . . . . . . . . . . . . . . 39 3.3 Complex Join Conditions . . . . . . . . . . . . . . . . . . . 41 3.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.4.1 Scalar Skew Experiments . . . . . . . . . . . . . . . 44 3.4.2 Zipfs Distribution . . . . . . . . . . . . . . . . . . 49 3.4.3 Non-Equijoin Experiments . . . . . . . . . . . . . . 50 3.4.4 Scalability Experiments . . . . . . . . . . . . . . . 52 3.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.5.1 Sampling . . . . . . . . . . . . . . . . . . . . . . . 55 3.5.2 Memory-Awareness . . . . . . . . . . . . . . . . . 58 3.5.3 Handling of Heavy Cells . . . . . . . . . . . . . . . 59 3.5.4 Existing Histograms . . . . . . . . . . . . . . . . . 60 3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 IV. Extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.1 Joining Multiple Relations in a MapReduce Job . . . . . . . 65 4.1.1 Example: SPARQL Basic Graph Pattern . . . . . . . 65 4.1.2 Example: Matrix Chain Multiplication . . . . . . . . 67 4.1.3 Single-Key Join and Multiple-Key Join Queries . . . 69 4.2 Skew Handling for Multi-Way Joins . . . . . . . . . . . . . 71 4.2.1 Skew Handling for SK-Join Queries . . . . . . . . . 71 4.2.2 Skew Handling for MK-Join Queires . . . . . . . . 72 4.3 Combinations of SK-Join and MK-Join . . . . . . . . . . . 74 4.3.1 Complex Queries . . . . . . . . . . . . . . . . . . . 74 4.3.2 Iteration-Based Algorithms . . . . . . . . . . . . . . 75 4.3.3 Replication-Based Algorithms . . . . . . . . . . . . 77 4.3.4 Iteration-Based vs. Replication-Based . . . . . . . . 78 4.4 Join-Key Selection Algorithms for Complex Queries . . . . 83 4.4.1 Greedy Key Selection . . . . . . . . . . . . . . . . 84 4.4.2 Multiple Key Selection . . . . . . . . . . . . . . . . 85 4.4.3 Hybrid Key Selection . . . . . . . . . . . . . . . . . 86 4.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . 87 4.5.1 SK-Join Experiments . . . . . . . . . . . . . . . . . 87 4.5.2 MK-Join Experiments . . . . . . . . . . . . . . . . 89 4.5.3 Analysis of TV Watching Logs . . . . . . . . . . . . 90 4.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 V. Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 5.1 Algorithms for SPARQL Basic Graph Pattern . . . . . . . . 94 5.1.1 MR-Selection . . . . . . . . . . . . . . . . . . . . . 95 5.1.2 MR-Join . . . . . . . . . . . . . . . . . . . . . . . 98 5.1.3 Performance Evaluation . . . . . . . . . . . . . . . 101 5.1.4 Discussion . . . . . . . . . . . . . . . . . . . . . . 105 5.2 Algorithms for Matrix Chain Multiplication . . . . . . . . . 107 5.2.1 Serial Two-Way Join (S2) . . . . . . . . . . . . . . 109 5.2.2 Parallel M-Way Join (P2, PM) . . . . . . . . . . . . 111 5.2.3 Serial Two-Way vs. Parallel M-Way . . . . . . . . . 115 5.2.4 Performance Evaluation . . . . . . . . . . . . . . . 116 5.2.5 Discussion . . . . . . . . . . . . . . . . . . . . . . 119 5.2.6 Extension: Embedded MapReduce . . . . . . . . . . 119 VI. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 초록 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133Docto

Graph Processing in Main-Memory Column Stores

Evermore, novel and traditional business applications leverage the advantages of a graph data model, such as the offered schema flexibility and an explicit representation of relationships between entities. As a consequence, companies are confronted with the challenge of storing, manipulating, and querying terabytes of graph data for enterprise-critical applications. Although these business applications operate on graph-structured data, they still require direct access to the relational data and typically rely on an RDBMS to keep a single source of truth and access. Existing solutions performing graph operations on business-critical data either use a combination of SQL and application logic or employ a graph data management system. For the first approach, relying solely on SQL results in poor execution performance caused by the functional mismatch between typical graph operations and the relational algebra. To the worse, graph algorithms expose a tremendous variety in structure and functionality caused by their often domain-specific implementations and therefore can be hardly integrated into a database management system other than with custom coding. Since the majority of these enterprise-critical applications exclusively run on relational DBMSs, employing a specialized system for storing and processing graph data is typically not sensible. Besides the maintenance overhead for keeping the systems in sync, combining graph and relational operations is hard to realize as it requires data transfer across system boundaries. A basic ingredient of graph queries and algorithms are traversal operations and are a fundamental component of any database management system that aims at storing, manipulating, and querying graph data. Well-established graph traversal algorithms are standalone implementations relying on optimized data structures. The integration of graph traversals as an operator into a database management system requires a tight integration into the existing database environment and a development of new components, such as a graph topology-aware optimizer and accompanying graph statistics, graph-specific secondary index structures to speedup traversals, and an accompanying graph query language. In this thesis, we introduce and describe GRAPHITE, a hybrid graph-relational data management system. GRAPHITE is a performance-oriented graph data management system as part of an RDBMS allowing to seamlessly combine processing of graph data with relational data in the same system. We propose a columnar storage representation for graph data to leverage the already existing and mature data management and query processing infrastructure of relational database management systems. At the core of GRAPHITE we propose an execution engine solely based on set operations and graph traversals. Our design is driven by the observation that different graph topologies expose different algorithmic requirements to the design of a graph traversal operator. We derive two graph traversal implementations targeting the most common graph topologies and demonstrate how graph-specific statistics can be leveraged to select the optimal physical traversal operator. To accelerate graph traversals, we devise a set of graph-specific, updateable secondary index structures to improve the performance of vertex neighborhood expansion. Finally, we introduce a domain-specific language with an intuitive programming model to extend graph traversals with custom application logic at runtime. We use the LLVM compiler framework to generate efficient code that tightly integrates the user-specified application logic with our highly optimized built-in graph traversal operators. Our experimental evaluation shows that GRAPHITE can outperform native graph management systems by several orders of magnitude while providing all the features of an RDBMS, such as transaction support, backup and recovery, security and user management, effectively providing a promising alternative to specialized graph management systems that lack many of these features and require expensive data replication and maintenance processes

Business Intelligence on Non-Conventional Data

The revolution in digital communications witnessed over the last decade had a significant impact on the world of Business Intelligence (BI). In the big data era, the amount and diversity of data that can be collected and analyzed for the decision-making process transcends the restricted and structured set of internal data that BI systems are conventionally limited to. This thesis investigates the unique challenges imposed by three specific categories of non-conventional data: social data, linked data and schemaless data. Social data comprises the user-generated contents published through websites and social media, which can provide a fresh and timely perception about people’s tastes and opinions. In Social BI (SBI), the analysis focuses on topics, meant as specific concepts of interest within the subject area. In this context, this thesis proposes meta-star, an alternative strategy to the traditional star-schema for modeling hierarchies of topics to enable OLAP analyses. The thesis also presents an architectural framework of a real SBI project and a cross-disciplinary benchmark for SBI. Linked data employ the Resource Description Framework (RDF) to provide a public network of interlinked, structured, cross-domain knowledge. In this context, this thesis proposes an interactive and collaborative approach to build aggregation hierarchies from linked data. Schemaless data refers to the storage of data in NoSQL databases that do not force a predefined schema, but let database instances embed their own local schemata. In this context, this thesis proposes an approach to determine the schema profile of a document-based database; the goal is to facilitate users in a schema-on-read analysis process by understanding the rules that drove the usage of the different schemata. A final and complementary contribution of this thesis is an innovative technique in the field of recommendation systems to overcome user disorientation in the analysis of a large and heterogeneous wealth of data