Adaptive P2P platform for data sharing

Ph.DDOCTOR OF PHILOSOPH

Managing cache for efficient query processing

Ph.DDOCTOR OF PHILOSOPH

Grid Data Management: Open Problems and New Issues

International audienceInitially developed for the scientific community, Grid computing is now gaining much interest in important areas such as enterprise information systems. This makes data management critical since the techniques must scale up while addressing the autonomy, dynamicity and heterogeneity of the data sources. In this paper, we discuss the main open problems and new issues related to Grid data management. We first recall the main principles behind data management in distributed systems and the basic techniques. Then we make precise the requirements for Grid data management. Finally, we introduce the main techniques needed to address these requirements. This implies revisiting distributed database techniques in major ways, in particular, using P2P techniques

Performance Optimization for Distributed Database Based on Cache Investment

As technology plays important role in every aspect of life especially in the industrial field, a vast amount of data is generated for controlling and monitoring tools and to help in system development. That results in an increased size of data, which affects the speed and performance of applications/programs. Here a design strategy is proposed to show how to improve both the speed and performance of computer applications by improving the performance of database queries. Key factors that determine computer performance (speed and performance of the processor) are the processor speed, the size of RAM, and the cache memory strategy for the processor. In this paper, we are introducing a solution that is proven to be a tool to increase the performance of queries. It will help to enhance the performance of database queries responsiveness irrespective of the database size. The proposed policy is built-up on the caching concepts i.e. cache investment. Cache investment is a method to combine query optimization and data placement. This work on the concept of investment looks beyond the performance of a single query and helps in achieving a better hit ratio in a long term for large database systems. This paper, discuss and explain the design, architecture and working of the proposed policy. The results show how this proposed policy helps in improving the performance of the database, especially relevant for today’s “big data” environment

Semantic Caching Framework: An FPGA-Based Application for IoT Security Monitoring

Security monitoring is one subdomain of cybersecurity which aims to guarantee the safety of systems, continuously monitoring unusual events. The development of Internet Of Things leads to huge amounts of information, being heterogeneous and requiring to be efficiently managed. Cloud Computing provides software and hardware resources for large scale data management. However, performances for sequences of on-line queries on long term historical data may be not compatible with the emergency security monitoring. This work aims to address this problem by proposing a semantic caching framework and its application to acceleration hardware with FPGA for fast- and accurate-enough logs processing for various data stores and execution engines

Identifying New Directions in Database Performance Tuning

Database performance tuning is a complex and varied active research topic. With enterprise relational database management systems still reliant on the same set-based relational concepts that defined early data management products, the disparity between the object-oriented application development model and the object-relational database model, called the object-relational impedance mismatch problem, is addressed by techniques such as object-relational mapping (ORM). However, this has resulted in generally poor query performance for SQL developed by object applications and an irregular fit with cost-based optimisation algorithms, and leads to questions about the need for the relational model to better adapt to ORM-generated queries. This paper discusses database performance optimisation developments and seeks to demonstrate that current database performance tuning approaches need re-examination. Proposals for further work include exploring concepts such as dynamic schema redefinition; query analysis and optimisation modelling driven by machine learning; and augmentation or replacement of the cost-based optimiser model

Supporting Efficient Database Processing in Mapreduce

Ph.DDOCTOR OF PHILOSOPH

Manutenção de Caches para sistemas OLAP

Dissertação de mestrado em Engenharia de InformáticaNos dias que correm a utilização de sistemas multidimensionais de dados faz parte do quotidiano de qualquer organização de média ou de grande dimensão. Este tipo de sistema, cuja principal finalidade consiste em auxiliar os seus utilizadores nas suas atividades de tomada de decisão, tem por principais características a flexibilidade na exploração de dados e a celeridade na disponibilização de informação. Apesar de todos os mecanismos já existentes, que contribuem para este objetivo, é, por vezes, muito difícil manter os níveis de desempenho desejados pelos utilizadores. Como consequência disto, foram estudadas outras formas para reduzir a carga imposta ao servidor multidimensional de dados – o servidor OLAP. Um destes mecanismos é a criação de caches que armazenam informação já consultada e que, aquando de um pedido, o satisfazem sem terem a necessidade de consultar a sua fonte. Devido à natureza dos utilizadores dos sistemas OLAP, é possível determinar com bastante precisão os seus padrões de acesso e de exploração, isto é, quais as consultas efetuadas por um determinado utilizador ao longo de um dado período de tempo. Levando esta análise para um pouco mais à frente é, ainda, possível prever com antecedência qual a sequência exata de consultas que será efetuada por um determinado utilizador quando este iniciar uma qualquer sessão de exploração de dados. Após esta fase de previsão, consegue-se decidir quais as queries que deverão ser pré-materializadas, armazenando-as numa cache, de forma a servir o maior número possível de pedidos do utilizador a partir desta. A técnica proposta nesta dissertação centra-se na problemática que gira em torno da simplificação do número de queries que deverão ser pré-materializadas. O objetivo final desta técnica consiste em manter um balanço positivo entre o tempo dispendido a realizar esta pré-materialização e o que seria gasto caso tudo fosse calculado apenas quando requisitado.Nowadays, the use of Multidimensional Data Systems has become a part of everyday actions in medium and large companies. This type of system, which concerns mainly in aiding it‟s users in the process of decision making, has a very large flexibility in data exploration and speed of response to queries. Despite all the existing techniques, it is sometimes, very hard to maintain such high levels of performance users demand. With the purpose of tackling these performance losses, other techniques where developed, wich try to reduce central data servers load. One of such mechanisms is the creation of OLAP caches that maintain previous queries and serve them upon subsequent requests without having to ask the central server. Due to OLAP Systems organization, it is possible to identify the characteristics of its users and its exploration patterns – what queries will a user submit during a session. It is, however, possible to go one step further, and to predict exactly wich data will be requested by a specific user and, more important, the sequence of those requests. This is called the prediction phase and is followed by the pre-materialization of views that correspond to the user‟s requests in the future. These views are then stored in the cache and served to the user in the appropriate time. The proposed technique‟s main goal consists in maintaining a positive ration between the time spent to predict and materialize the views, and the time that would be spent if no prediction had been done