Benchmarking BigSQL Systems

Elame suurandmete ajastul. Tänapäeval on olemas suurandmete töötlemise süsteemid, mis on võimelised haldama sadu terabaite ja petabaite andmeid. Need süsteemid töötlevad andmehulki, mis on liiga suured traditsiooniliste andmebaasisüsteemide jaoks. Mõned neist süsteemidest sisaldavad SQL keeli andmehoidlaga suhtlemiseks. Nendel süsteemidel, mida nimetatakse ka BigSQL süsteemideks, on mõned omadused, mis teevad nende andmete hoidmist ja haldamist unikaalseks. Süsteemide paremaks mõistmiseks on vajalik nende jõudluse ja omaduste uuring. Antud töö sisaldab BigSQL süsteemide jõudluse võrdlusuuringut. Valitud BigSQL süsteemdiega viiakse läbi standardiseeritud jõudlustestid ja eksperimentidest saadud tulemusi analüüsitakse. Töö eesmärgiks on seletada paremini lahti valitud BigSQL süsteemide omadusi ja käitumist.We live in the era of BigData. We now have BigData systems which are able to manage data in volumes of hundreds of terabytes and petabytes. These BigData systems handle data sizes which are too large for traditional database systems to handle. Some of these BigData systems now provide SQL syntax for interacting with their store. These BigData systems, referred to as BigSQL systems, possess certain features which make them unique in how they manage the stored. A study into the performances and characteristics of these BigSQL systems is necessary in order to better understand these systems. This thesis provides that study into the performance of these BigSQL systems. In this thesis, we perform standardized benchmark experiments against some selected BigSQL systems and then analyze the performances of these systems based on the results of the experiments. The output of this thesis study will provide an understanding of the features and behaviors of the BigSQL systems

EasyBDI: integração automática de big data e consultas analíticas de alto nível

Abstract The emergence of new areas, such as the internet of things, which require access to the latest data for data analytics and decision-making environments, created constraints for the execution of analytical queries on traditional data warehouse architectures. In addition, the increase of semi-structure and unstructured data led to the creation of new databases to deal with these types of data, namely, NoSQL databases. This led to the information being stored in several different systems, each with more suitable characteristics for different use cases, which created difficulties in accessing data that are now spread across various systems with different models and characteristics. In this work, a system capable of performing analytical queries in real time on distributed and heterogeneous data sources is proposed: EasyBDI. The system is capable of integrating data logically, without materializing data, creating an overview of the data, thus offering an abstraction over the distribution and heterogeneity of data sources. Queries are executed interactively on data sources, which means that the most recent data will always be used in queries. This system presents a user interface that helps in the configuration of data sources, and automatically proposes a global schema that presents a generic and simplified view of the data, which can be modified by the user. The system allows the creation of multiple star schemas from the global schema. Finally, analytical queries are also made through a user interface that uses drag-and-drop elements. EasyBDI is able to solve recent problems by using recent solutions, hiding the details of several data sources, at the same time that allows users with less knowledge of databases to also be able to perform real-time analytical queries over distributed and heterogeneous data sources.O aparecimento de novas áreas, como a Internet das Coisas, que requerem o acesso aos dados mais recentes para ambientes de tomada de decisão, criou constrangimentos na execução de consultas analíticas usando as arquiteturas tradicionais de data warehouses. Adicionalmente, o aumento de dados semi-estruturados e não estruturados levou a que outras bases de dados fossem criadas para lidar com esse tipo de dados, nomeadamente bases NoSQL. Isto levou a que a informação seja armazenada em sistemas com características distintas e especializados em diferentes casos de uso, criando dificuldades no acesso aos dados que estão agora espalhados por vários sistemas com modelos e características distintas. Neste trabalho, propõe-se um sistema capaz de efetuar consultas analíticas em tempo real sobre fontes de dados distribuídas e heterogéneas: o EasyBDI. O sistema é capaz de integrar dados logicamente, sem materializar os dados, criando uma vista geral dos dados que oferece uma abstração sobre a distribuição e heterogeneidade das fontes de dados. As consultas são executadas interativamente nas fontes de dados, o que significa que os dados mais recentes serão sempre usados nas consultas. Este sistema apresenta uma interface de utilizador que ajuda na configuração de fontes de dados, e propõe automaticamente um esquema global que apresenta a vista genérica e simplificada dos dados, podendo ser modificado pelo utilizador. O sistema permite a criação de múltiplos esquema em estrela a partir do esquema global. Por fim, a realização de consultas analíticas é feita também através de uma interface de utilizador que recorre ao drag-and-drop de elementos. O EasyBDI é capaz de resolver problemas recentes, utilizando também soluções recentes, escondendo os detalhes de diversas fontes de dados, ao mesmo tempo que permite que utilizadores com menos conhecimentos em bases de dados possam também realizar consultas analíticas em tempo-real sobre fontes de dados distribuídas e heterogéneas.Mestrado em Engenharia Informátic

Federated Query Processing over Heterogeneous Data Sources in a Semantic Data Lake

Data provides the basis for emerging scientific and interdisciplinary data-centric applications with the potential of improving the quality of life for citizens. Big Data plays an important role in promoting both manufacturing and scientific development through industrial digitization and emerging interdisciplinary research. Open data initiatives have encouraged the publication of Big Data by exploiting the decentralized nature of the Web, allowing for the availability of heterogeneous data generated and maintained by autonomous data providers. Consequently, the growing volume of data consumed by different applications raise the need for effective data integration approaches able to process a large volume of data that is represented in different format, schema and model, which may also include sensitive data, e.g., financial transactions, medical procedures, or personal data. Data Lakes are composed of heterogeneous data sources in their original format, that reduce the overhead of materialized data integration. Query processing over Data Lakes require the semantic description of data collected from heterogeneous data sources. A Data Lake with such semantic annotations is referred to as a Semantic Data Lake. Transforming Big Data into actionable knowledge demands novel and scalable techniques for enabling not only Big Data ingestion and curation to the Semantic Data Lake, but also for efficient large-scale semantic data integration, exploration, and discovery. Federated query processing techniques utilize source descriptions to find relevant data sources and find efficient execution plan that minimize the total execution time and maximize the completeness of answers. Existing federated query processing engines employ a coarse-grained description model where the semantics encoded in data sources are ignored. Such descriptions may lead to the erroneous selection of data sources for a query and unnecessary retrieval of data, affecting thus the performance of query processing engine. In this thesis, we address the problem of federated query processing against heterogeneous data sources in a Semantic Data Lake. First, we tackle the challenge of knowledge representation and propose a novel source description model, RDF Molecule Templates, that describe knowledge available in a Semantic Data Lake. RDF Molecule Templates (RDF-MTs) describes data sources in terms of an abstract description of entities belonging to the same semantic concept. Then, we propose a technique for data source selection and query decomposition, the MULDER approach, and query planning and optimization techniques, Ontario, that exploit the characteristics of heterogeneous data sources described using RDF-MTs and provide a uniform access to heterogeneous data sources. We then address the challenge of enforcing privacy and access control requirements imposed by data providers. We introduce a privacy-aware federated query technique, BOUNCER, able to enforce privacy and access control regulations during query processing over data sources in a Semantic Data Lake. In particular, BOUNCER exploits RDF-MTs based source descriptions in order to express privacy and access control policies as well as their automatic enforcement during source selection, query decomposition, and planning. Furthermore, BOUNCER implements query decomposition and optimization techniques able to identify query plans over data sources that not only contain the relevant entities to answer a query, but also are regulated by policies that allow for accessing these relevant entities. Finally, we tackle the problem of interest based update propagation and co-evolution of data sources. We present a novel approach for interest-based RDF update propagation that consistently maintains a full or partial replication of large datasets and deal with co-evolution

Flexible Integration and Efficient Analysis of Multidimensional Datasets from the Web

If numeric data from the Web are brought together, natural scientists can compare climate measurements with estimations, financial analysts can evaluate companies based on balance sheets and daily stock market values, and citizens can explore the GDP per capita from several data sources. However, heterogeneities and size of data remain a problem. This work presents methods to query a uniform view - the Global Cube - of available datasets from the Web and builds on Linked Data query approaches

Biomedical data retrieval utilizing textual data in a gene expression database by Richard Lu, MD.

Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 68-74).Background: The commoditization of high-throughput gene expression sequencing and microarrays has led to a proliferation in both the amount of genomic and clinical data that is available. Descriptive textual information deposited with gene expression data in the Gene Expression Omnibus (GEO) is an underutilized resource because the textual information is unstructured and difficult to query. Rendering this information in a structured format utilizing standard medical terms would facilitate better searching and data reuse. Such a procedure would significantly increase the clinical utility of biomedical data repositories. Methods: The thesis is divided into two sections. The first section compares how well four medical terminologies were able to represent textual information deposited in GEO. The second section implements free-text search and faceted search and evaluates how well they are able to answer clinical queries with varying levels of complexity. Part I: 120 samples were randomly extracted from samples deposited in the GEO database from six clinical domains-breast cancer, colon cancer, rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), type I diabetes mellitus (IDDM), and asthma. These samples were previously annotated manually and structured textual information was obtained in a tag:value format. Data was mapped to four different controlled terminologies: NCI Thesaurus, MeSH, SNOMED-CT, and ICD- 10. The samples were assigned a score on a three-point scale that was based on how well the terminology was able to represent descriptive textual information. Part II: Faceted and free-text search tools were implemented, with 300 GEO samples included for querying. Eight natural language search questions were selected randomly from scientific journals. Academic researchers were recruited and asked to use the faceted and free-text search tools to locate samples matching the question criteria. Precision, recall, F-score, and search time were compared and analyzed for both free-text and faceted search. Results: The results show that the NCI Thesaurus consistently ranked as the most comprehensive terminology across all domains while ICD-10 consistently ranked as the least comprehensive. Using NCI Thesaurus to augment the faceted search tool, each researcher was able to reach 100% precision and recall (F-score 1.0) for each of the eight search questions. Using free-text search, test users averaged 22.8% precision, 60.7% recall, and an F-score of 0.282. The mean search time per question using faceted search and free-text search were 116.7 seconds, and 138.4 seconds, respectively. The difference between search time was not statistically significant (p=0. 734). However, paired t-test analysis showed a statistically signficant difference between the two search strategies with respect to precision (p=O.001), recall (p=O.042), and F-score (p<0. 001). Conclusion: This work demonstrates that biomedical terms included in a gene expression database can be adequately expressed using the NCI Thesaurus. It also shows that faceted searching using a controlled terminology is superior to conventional free-text searching when answering queries of varying levels of complexity.S.M