An online data access prediction and optimization approach for distributed systems

Current scientific applications have been producing large amounts of data. The processing, handling and analysis of such data require large-scale computing infrastructures such as clusters and grids. In this area, studies aim at improving the performance of data-intensive applications by optimizing data accesses. In order to achieve this goal, distributed storage systems have been considering techniques of data replication, migration, distribution, and access parallelism. However, the main drawback of those studies is that they do not take into account application behavior to perform data access optimization. This limitation motivated this paper which applies strategies to support the online prediction of application behavior in order to optimize data access operations on distributed systems, without requiring any information on past executions. In order to accomplish such a goal, this approach organizes application behaviors as time series and, then, analyzes and classifies those series according to their properties. By knowing properties, the approach selects modeling techniques to represent series and perform predictions, which are, later on, used to optimize data access operations. This new approach was implemented and evaluated using the OptorSim simulator, sponsored by the LHC-CERN project and widely employed by the scientific community. Experiments confirm this new approach reduces application execution time in about 50 percent, specially when handling large amounts of data.

Query Rewriting Optimisation Techniques for Ontology-Based Data Access

Ontology-Based Data Access (OBDA) permite el acceso a diferentes tipos de fuentes de datos (tradicionalmente bases de datos) usando un modelo más abstracto proporcionado por una ontología. La reescritura de consultas (query rewriting) usa una ontología para reescribir una consulta en una consulta reescrita que puede ser evaluada en la fuente de datos. Las consultas reescritas recuperan las respuestas que están implicadas por la combinación de los datos explicitamente almacenados en la fuente de datos, la consulta original y la ontología. Al trabajar sólo sobre las queries, la reescritura de consultas permite OBDA sobre cualquier fuente de datos que puede ser consultada, independientemente de las posibilidades para modificarla. Sin embargo, producir y evaluar las consultas reescritas son procesos costosos que suelen volverse más complejos conforme la expresividad y tamaño de la ontología y las consultas aumentan. En esta tesis exploramos distintas optimizaciones que peuden ser realizadas tanto en el proceso de reescritura como en las consultas reescritas para mejorar la aplicabilidad de OBDA en contextos realistas. Nuestra contribución técnica principal es un sistema de reescritura de consultas que implementa las optimizaciones presentadas en esta tesis. Estas optimizaciones son las contribuciones principales de la tesis y se pueden agrupar en tres grupos diferentes: -optimizaciones que se pueden aplicar al considerar los predicados en la ontología que no están realmente mapeados con las fuentes de datos. -optimizaciones en ingeniería que se pueden aplicar al manejar el proceso de reescritura de consultas en una forma que permite reducir la carga computacional del proceso de generación de consultas reescritas. -optimizaciones que se pueden aplicar al considerar metainformación adicional acerca de las características de la ABox. En esta tesis proporcionamos demostraciones formales acerca de la corrección y completitud de las optimizaciones propuestas, y una evaluación empírica acerca del impacto de estas optimizaciones. Como contribución adicional, parte de este enfoque empírico, proponemos un banco de pruebas (benchmark) para la evaluación de los sistemas de reescritura de consultas. Adicionalmente, proporcionamos algunas directrices para la creación y expansión de esta clase de bancos de pruebas. ABSTRACT Ontology-Based Data Access (OBDA) allows accessing different kinds of data sources (traditionally databases) using a more abstract model provided by an ontology. Query rewriting uses such ontology to rewrite a query into a rewritten query that can be evaluated on the data source. The rewritten queries retrieve the answers that are entailed by the combination of the data explicitly stored in the data source, the original query and the ontology. However, producing and evaluating the rewritten queries are both costly processes that become generally more complex as the expressiveness and size of the ontology and queries increase. In this thesis we explore several optimisations that can be performed both in the rewriting process and in the rewritten queries to improve the applicability of OBDA in real contexts. Our main technical contribution is a query rewriting system that implements the optimisations presented in this thesis. These optimisations are the core contributions of the thesis and can be grouped into three different groups: -optimisations that can be applied when considering the predicates in the ontology that are actually mapped to the data sources. -engineering optimisations that can be applied by handling the process of query rewriting in a way that permits to reduce the computational load of the query generation process. -optimisations that can be applied when considering additional metainformation about the characteristics of the ABox. In this thesis we provide formal proofs for the correctness of the proposed optimisations, and an empirical evaluation about the impact of the optimisations. As an additional contribution, part of this empirical approach, we propose a benchmark for the evaluation of query rewriting systems. We also provide some guidelines for the creation and expansion of this kind of benchmarks.

CVISN safety information exchange for commercial vehicles in Connecticut : a case study : increasing inspection efficiency through wireless data access at the roadside.

"EDL# 13981"--P. [4] of cover.

Action plan II : regional monitoring, data access, and interagency coordination /

Mode of access: Internet.

A semantic paradigm for intelligent data access

An implementation of Sem-ODB—a database management system based on the Semantic Binary Model is presented. A metaschema of Sem-ODB database as well as the top-level architecture of the database engine is defined. A new benchmarking technique is proposed which allows databases built on different database models to compete fairly. This technique is applied to show that Sem-ODB has excellent efficiency comparing to a relational database on a certain class of database applications. A new semantic benchmark is designed which allows evaluation of the performance of the features characteristic of semantic database applications. An application used in the benchmark represents a class of problems requiring databases with sparse data, complex inheritances and many-to-many relations. Such databases can be naturally accommodated by semantic model. A fixed predefined implementation is not enforced allowing the database designer to choose the most efficient structures available in the DBMS tested. The results of the benchmark are analyzed. ^ A new high-level querying model for semantic databases is defined. It is proven adequate to serve as an efficient native semantic database interface, and has several advantages over the existing interfaces. It is optimizable and parallelizable, supports the definition of semantic userviews and the interoperability of semantic databases with other data sources such as World Wide Web, relational, and object-oriented databases. The query is structured as a semantic database schema graph with interlinking conditionals. The query result is a mini-database, accessible in the same way as the original database. The paradigm supports and utilizes the rich semantics and inherent ergonomics of semantic databases. ^ The analysis and high-level design of a system that exploits the superiority of the Semantic Database Model to other data models in expressive power and ease of use to allow uniform access to heterogeneous data sources such as semantic databases, relational databases, web sites, ASCII files, and others via a common query interface is presented. The Sem-ODB engine is used to control all the data sources combined under a unified semantic schema. A particular application of the system to provide an ODBC interface to the WWW as a data source is discussed. ^

Studies of CMS data access patterns with machine learning techniques

This thesis presents a study of the Grid data access patterns in distributed analysis in the CMS experiment at the LHC accelerator. This study ranges from the deep analysis of the historical patterns of access to the most relevant data types in CMS, to the exploitation of a supervised Machine Learning classification system to set-up a machinery able to eventually predict future data access patterns - i.e. the so-called dataset “popularity” of the CMS datasets on the Grid - with focus on specific data types. All the CMS workflows run on the Worldwide LHC Computing Grid (WCG) computing centers (Tiers), and in particular the distributed analysis systems sustains hundreds of users and applications submitted every day. These applications (or “jobs”) access different data types hosted on disk storage systems at a large set of WLCG Tiers. The detailed study of how this data is accessed, in terms of data types, hosting Tiers, and different time periods, allows to gain precious insight on storage occupancy over time and different access patterns, and ultimately to extract suggested actions based on this information (e.g. targetted disk clean-up and/or data replication). In this sense, the application of Machine Learning techniques allows to learn from past data and to gain predictability potential for the future CMS data access patterns. Chapter 1 provides an introduction to High Energy Physics at the LHC. Chapter 2 describes the CMS Computing Model, with special focus on the data management sector, also discussing the concept of dataset popularity. Chapter 3 describes the study of CMS data access patterns with different depth levels. Chapter 4 offers a brief introduction to basic machine learning concepts and gives an introduction to its application in CMS and discuss the results obtained by using this approach in the context of this thesis.

Ontology-based access to sensor data streams

Sensor networks are increasingly becoming one of the main sources of Big Data on the Web. However, the observations that they produce are made available with heterogeneous schemas, vocabularies and data formats, making it difficult to share and reuse these data for other purposes than those for which they were originally set up. In this thesis we address these challenges, considering how we can transform streaming raw data to rich ontology-based information that is accessible through continuous queries for streaming data. Our main contribution is an ontology-based approach for providing data access and query capabilities to streaming data sources, allowing users to express their needs at a conceptual level, independent of implementation and language-specific details. We introduce novel query rewriting and data translation techniques that rely on mapping definitions relating streaming data models to ontological concepts. Specific contributions include: • The syntax and semantics of the SPARQLStream query language for ontologybased data access, and a query rewriting approach for transforming SPARQLStream queries into streaming algebra expressions. • The design of an ontology-based streaming data access engine that can internally reuse an existing data stream engine, complex event processor or sensor middleware, using R2RML mappings for defining relationships between streaming data models and ontology concepts. Concerning the sensor metadata of such streaming data sources, we have investigated how we can use raw measurements to characterize streaming data, producing enriched data descriptions in terms of ontological models. Our specific contributions are: • A representation of sensor data time series that captures gradient information that is useful to characterize types of sensor data. • A method for classifying sensor data time series and determining the type of data, using data mining techniques, and a method for extracting semantic sensor metadata features from the time series.

On the cloud deployment of a session abstraction for service/data aggregation

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Managing Packet Data (Sessions) in 3G Mobile Networks

Tällä hetkellä kolmannen sukupolven matkapuhelinjärjestelmät ovat siirtyneet kaupalliseen vaiheeseen. Universal Mobile Telecommunication System (UMTS) on eräs kolmannen sukupolven matkapuhelinjärjestelmä, jota tullaan käyttämään Euroopassa. Diplomityön päämääränä on tutkia, kuinka pakettivälitteistä tiedonsiirtoa hallitaan UMTS - verkoissa. Diplomityö antaa yleiskuvan toisen sukupolven matkapuhelinjärjestelmien datapalveluiden kehityksestä kolmannen sukupolven nopeisiin matkapuhelinjärjestelmiin. Pakettivälitteisen verkon verkkoarkkitehtuuri on esitetty sekä sen, diplomityön kannalta, tärkeimpien osien toiminnallisuus on selvitetty. Myös pakettipohjaisten datayhteyksien eli istuntojen muodostaminen ja vapauttaminen sekä aktiivisen yhteyden ominaisuuksien muokkaaminen on esitetty tässä diplomityössä. Yhteydenhallintaprotokolla, Session Management (SM), on yksi protokolla, joka osallistuu pakettidatayhteyden hallintaan. SM -protokolla on käsitelty työssä yksityiskohtaisesti. SM -protokollan SDL toteutus on esitetty diplomityön käytännönosassa

Towards Interoperability in P2P World: an Indexing Middleware for Multi-Protocol Peer-to-Peer Data Sharing

Despite the abundant availability,of protocols and application for peer-to-peer file sharing, several drawbacks are still present in the field. Among most notable drawbacks is the lack of a simple and interoperable way to share information among independent peer-to-peer networks. Another drawback is the requirement that the shared content can be accessed only by a limited number of compatible applications, making impossible their access to others applications and system. In this work we present a new approach for peer-to-peer data indexing, focused on organization and retrieval of metadata which describes the shared content. This approach results in a common and interoperable infrastructure, which provides a transparent access to data shared on multiple data sharing networks via a simple API. The proposed approach is evaluated using a case study, implemented as a cross-platform extension to Mozilla Fir fox browser; and demonstrates the advantages of such interoperability over conventional distributed data access strategies.