Efficient successor retrieval operations for aggregate query processing on clustered road networks

Cataloged from PDF version of article.Get-Successors (GS) which retrieves all successors of a junction is a kernel operation used to facilitate aggregate computations in road network queries. Efficient implementation of the GS operation is crucial since the disk access cost of this operation constitutes a considerable portion of the total query processing cost. Firstly, we propose a new successor retrieval operation Get-Unevaluated-Successors (GUS), which retrieves only the unevaluated successors of a given junction. The GUS operation is an efficient implementation of the GS operation, where the candidate successors to be retrieved are pruned according to the properties and state of the algorithm. Secondly, we propose a hypergraph-based model for clustering successively retrieved junctions by the GUS operations to the same pages. The proposed model utilizes query logs to correctly capture the disk access cost of GUS operations. The proposed GUS operation and associated clustering model are evaluated for two different instances of GUS operations which typically arise in Dijkstra's single source shortest path algorithm and incremental network expansion framework. Our simulation results show that the proposed successor retrieval operation together with the proposed clustering hypergraph model is quite effective in reducing the number of disk accesses in query processing. (C) 2010 Published by Elsevier Inc

Storage and access schemes for aggregate query processing on road networks

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2009.Thesis (Ph.D.) -- Bilkent University, 2009.Includes bibliographical references leaves 103-111.A well-known example of spatial networks is road networks, which form an integral part of many geographic information system applications, such as locationbased services, intelligent traveling systems, vehicle telematics, and locationaware advertising. In practice, road network data is too large to fit into the volatile memory. A considerable portion of the data must be stored on the secondary storage since several spatial and non-spatial attributes as well as points-ofinterest data are associated with junctions and links. In network query processing, the spatial coherency that exists in accessing data leads to a temporal coherency; in this way, connected junctions are accessed almost concurrently. Taking this fact into consideration, it seems reasonable to place the data associated with connected junctions in the same disk pages so that the data can be fetched to the memory with fewer disk accesses. We show that the state-of-the-art clustering graph model for allocation of data to disk pages is not able to correctly capture the disk access cost of successor retrieval operations. We propose clustering models based on hypergraph partitioning, which correctly encapsulate the spatial and temporal coherency in query processing via the utilization of query logs in order to minimize the number of disk accesses during aggregate query processing. We introduce the link-based storage scheme for road networks as an alternative to the widely used junction-based storage scheme. We present GetUnevaluated-Successors (GUS) as a new successor retrieval operation for network queries, where the candidate successors to be retrieved are pruned during processing a query. We investigate two different instances of GUS operation: the Get-unProcessed-Successors operation typically arises in Dijkstra’s single source shortest path algorithm, and the Get-unVisited-Successors operation typically arises in the incremental network expansion framework. The simulation resultsshow that our storage and access schemes utilizing the proposed clustering hypergraph models are quite effective in reducing the number of disk accesses during aggregate query processing.Demir, EnginPh.D

Replicated partitioning for undirected hypergraphs

Cataloged from PDF version of article.Hypergraph partitioning (HP) and replication are diverse but powerful tools that are traditionally applied separately to minimize the costs of parallel and sequential systems that access related data or process related tasks. When combined together, these two techniques have the potential of achieving significant improvements in performance of many applications. In this study, we provide an approach involving a tool that simultaneously performs replication and partitioning of the vertices of an undirected hypergraph whose vertices represent data and nets represent task dependencies among these data. In this approach, we propose an iterative-improvement-based replicated bipartitioning heuristic, which is capable of move, replication, and unreplication of vertices. In order to utilize our replicated bipartitioning heuristic in a recursive bipartitioning framework, we also propose appropriate cut-net removal, cut-net splitting, and pin selection algorithms to correctly encapsulate the two most commonly used cutsize metrics. We embed our replicated bipartitioning scheme into the state-of-the-art multilevel HP tool PaToH to provide an effective and efficient replicated HP tool, rpPaToH. The performance of the techniques proposed and the tools developed is tested over the undirected hypergraphs that model the communication costs of parallel query processing in information retrieval systems. Our experimental analysis indicates that the proposed technique provides significant improvements in the quality of the partitions, especially under low replication ratios. (C) 2012 Elsevier Inc. All rights reserved

Efficient Analysis in Multimedia Databases

The rapid progress of digital technology has led to a situation where computers have become ubiquitous tools. Now we can find them in almost every environment, be it industrial or even private. With ever increasing performance computers assumed more and more vital tasks in engineering, climate and environmental research, medicine and the content industry. Previously, these tasks could only be accomplished by spending enormous amounts of time and money. By using digital sensor devices, like earth observation satellites, genome sequencers or video cameras, the amount and complexity of data with a spatial or temporal relation has gown enormously. This has led to new challenges for the data analysis and requires the use of modern multimedia databases. This thesis aims at developing efficient techniques for the analysis of complex multimedia objects such as CAD data, time series and videos. It is assumed that the data is modeled by commonly used representations. For example CAD data is represented as a set of voxels, audio and video data is represented as multi-represented, multi-dimensional time series. The main part of this thesis focuses on finding efficient methods for collision queries of complex spatial objects. One way to speed up those queries is to employ a cost-based decompositioning, which uses interval groups to approximate a spatial object. For example, this technique can be used for the Digital Mock-Up (DMU) process, which helps engineers to ensure short product cycles. This thesis defines and discusses a new similarity measure for time series called threshold-similarity. Two time series are considered similar if they expose a similar behavior regarding the transgression of a given threshold value. Another part of the thesis is concerned with the efficient calculation of reverse k-nearest neighbor (RkNN) queries in general metric spaces using conservative and progressive approximations. The aim of such RkNN queries is to determine the impact of single objects on the whole database. At the end, the thesis deals with video retrieval and hierarchical genre classification of music using multiple representations. The practical relevance of the discussed genre classification approach is highlighted with a prototype tool that helps the user to organize large music collections. Both the efficiency and the effectiveness of the presented techniques are thoroughly analyzed. The benefits over traditional approaches are shown by evaluating the new methods on real-world test datasets

Data Storage and Dissemination in Pervasive Edge Computing Environments

Nowadays, smart mobile devices generate huge amounts of data in all sorts of gatherings. Much of that data has localized and ephemeral interest, but can be of great use if shared among co-located devices. However, mobile devices often experience poor connectivity, leading to availability issues if application storage and logic are fully delegated to a remote cloud infrastructure. In turn, the edge computing paradigm pushes computations and storage beyond the data center, closer to end-user devices where data is generated and consumed. Hence, enabling the execution of certain components of edge-enabled systems directly and cooperatively on edge devices. This thesis focuses on the design and evaluation of resilient and efficient data storage and dissemination solutions for pervasive edge computing environments, operating with or without access to the network infrastructure. In line with this dichotomy, our goal can be divided into two specific scenarios. The first one is related to the absence of network infrastructure and the provision of a transient data storage and dissemination system for networks of co-located mobile devices. The second one relates with the existence of network infrastructure access and the corresponding edge computing capabilities. First, the thesis presents time-aware reactive storage (TARS), a reactive data storage and dissemination model with intrinsic time-awareness, that exploits synergies between the storage substrate and the publish/subscribe paradigm, and allows queries within a specific time scope. Next, it describes in more detail: i) Thyme, a data storage and dis- semination system for wireless edge environments, implementing TARS; ii) Parsley, a flexible and resilient group-based distributed hash table with preemptive peer relocation and a dynamic data sharding mechanism; and iii) Thyme GardenBed, a framework for data storage and dissemination across multi-region edge networks, that makes use of both device-to-device and edge interactions. The developed solutions present low overheads, while providing adequate response times for interactive usage and low energy consumption, proving to be practical in a variety of situations. They also display good load balancing and fault tolerance properties.Resumo Hoje em dia, os dispositivos móveis inteligentes geram grandes quantidades de dados em todos os tipos de aglomerações de pessoas. Muitos desses dados têm interesse loca- lizado e efêmero, mas podem ser de grande utilidade se partilhados entre dispositivos co-localizados. No entanto, os dispositivos móveis muitas vezes experienciam fraca co- nectividade, levando a problemas de disponibilidade se o armazenamento e a lógica das aplicações forem totalmente delegados numa infraestrutura remota na nuvem. Por sua vez, o paradigma de computação na periferia da rede leva as computações e o armazena- mento para além dos centros de dados, para mais perto dos dispositivos dos utilizadores finais onde os dados são gerados e consumidos. Assim, permitindo a execução de certos componentes de sistemas direta e cooperativamente em dispositivos na periferia da rede. Esta tese foca-se no desenho e avaliação de soluções resilientes e eficientes para arma- zenamento e disseminação de dados em ambientes pervasivos de computação na periferia da rede, operando com ou sem acesso à infraestrutura de rede. Em linha com esta dico- tomia, o nosso objetivo pode ser dividido em dois cenários específicos. O primeiro está relacionado com a ausência de infraestrutura de rede e o fornecimento de um sistema efêmero de armazenamento e disseminação de dados para redes de dispositivos móveis co-localizados. O segundo diz respeito à existência de acesso à infraestrutura de rede e aos recursos de computação na periferia da rede correspondentes. Primeiramente, a tese apresenta armazenamento reativo ciente do tempo (ARCT), um modelo reativo de armazenamento e disseminação de dados com percepção intrínseca do tempo, que explora sinergias entre o substrato de armazenamento e o paradigma pu- blicação/subscrição, e permite consultas num escopo de tempo específico. De seguida, descreve em mais detalhe: i) Thyme, um sistema de armazenamento e disseminação de dados para ambientes sem fios na periferia da rede, que implementa ARCT; ii) Pars- ley, uma tabela de dispersão distribuída flexível e resiliente baseada em grupos, com realocação preventiva de nós e um mecanismo de particionamento dinâmico de dados; e iii) Thyme GardenBed, um sistema para armazenamento e disseminação de dados em redes multi-regionais na periferia da rede, que faz uso de interações entre dispositivos e com a periferia da rede. As soluções desenvolvidas apresentam baixos custos, proporcionando tempos de res- posta adequados para uso interativo e baixo consumo de energia, demonstrando serem práticas nas mais diversas situações. Estas soluções também exibem boas propriedades de balanceamento de carga e tolerância a faltas

Streaming the Web: Reasoning over dynamic data.

In the last few years a new research area, called stream reasoning, emerged to bridge the gap between reasoning and stream processing. While current reasoning approaches are designed to work on mainly static data, the Web is, on the other hand, extremely dynamic: information is frequently changed and updated, and new data is continuously generated from a huge number of sources, often at high rate. In other words, fresh information is constantly made available in the form of streams of new data and updates. Despite some promising investigations in the area, stream reasoning is still in its infancy, both from the perspective of models and theories development, and from the perspective of systems and tools design and implementation. The aim of this paper is threefold: (i) we identify the requirements coming from different application scenarios, and we isolate the problems they pose; (ii) we survey existing approaches and proposals in the area of stream reasoning, highlighting their strengths and limitations; (iii) we draw a research agenda to guide the future research and development of stream reasoning. In doing so, we also analyze related research fields to extract algorithms, models, techniques, and solutions that could be useful in the area of stream reasoning. © 2014 Elsevier B.V. All rights reserved

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

Generic analysis support for understanding, evaluating and comparing enterprise architecture models

Enterprise Architecture Management (EAM) is one mean to deal with the increasing complexity of today’s IT landscapes. Architectural models are used within EAM to describe the business processes, the used applications, the required infrastructure as well as the dependencies between them. The creation of those models is expensive, since the whole organization and therewith a large amount of data has to be considered. It is important to make use of these models and reuse them for planning purposes and decision making. The models are a solid foundation for various kinds of analyses that support the understanding, evaluation and comparisons of them. Analyses can approximate the effects of the retirement of an application or of a server failure. It is also possible to quantify the models using metrics like the IT coverage of business processes or the workload of a server. The generation of views sets the focus on a specific aspect of the model. An example is the limitation to the processes and applications of a specific organization unit. Architectural models can also be used for planning purposes. The development of a target architecture is supported by identifying weak points and evaluating planning scenarios. Current approaches for EAM analysis are typically isolated ones, addressing only a limited subset of the different analysis goals. An integrated approach that covers the different information demands of the stakeholders is missing. Additionally, the analysis approaches are highly dependent on the utilized meta model. This is a serious problem since the EAM domain is characterized by a large variety of frameworks and meta models. In this thesis, we propose a generic framework that supports the different analysis activities during EAM. We develop the required techniques for the specification and execution of analyses, independently from the utilized meta model. An analysis language is implemented for the definition and customization of the analyses according to the current needs of the stakeholder. Thereby, we focus on reuse and a generic definition. We utilize a generic representation format to be able to abstract from the great variety of used meta models in the EAM domain. The execution of the analyses is done with Semantic Web Technologies and data-flow based model analysis. The framework is applied for the identification of weak points as well as the evaluation of planning scenarios regarding consistency of changes and goal fulfillment. Two methods are developed for these tasks, as well as respective analysis support is identified and implemented. These are, for example, a change impact analysis, specific metrics or the scoping of the architectural model according to different aspects. Finally, the coverage of the framework regarding existing EA analysis approaches is determined with a scenario-based evaluation. The applicability and relevance of the language and of the proposed methods is proved within three large case studies