Engineering Agile Big-Data Systems

To be effective, data-intensive systems require extensive ongoing customisation to reflect changing user requirements, organisational policies, and the structure and interpretation of the data they hold. Manual customisation is expensive, time-consuming, and error-prone. In large complex systems, the value of the data can be such that exhaustive testing is necessary before any new feature can be added to the existing design. In most cases, the precise details of requirements, policies and data will change during the lifetime of the system, forcing a choice between expensive modification and continued operation with an inefficient design.Engineering Agile Big-Data Systems outlines an approach to dealing with these problems in software and data engineering, describing a methodology for aligning these processes throughout product lifecycles. It discusses tools which can be used to achieve these goals, and, in a number of case studies, shows how the tools and methodology have been used to improve a variety of academic and business systems

Post Boost Track Processing Using Conventional DBMS Software

The design of air defence, traditional command control system is very challenging which has been used with basic methodologies. Traditional design is associated with unstructured and uncorrelated data and requires huge lines of code using hard disk drive (HDD) in the system. Hence an attempt was made for a better simplified database management system (DBMS) software data access methodology, which processed the incoming airborne data, message in RDBMS database to achieve full automation on real-time. The transaction is accomplished through SQL pass through method from the host decision making system into database. An algorithm of track identification during midcourse track separation was undertaken for prototype development on DBMS data access methodology. In this methodology Oracle C++ calls interface embedded query call was used from the host interface system. The purpose of this development was to find a comparison of online process timing between HDD and SSD using commercial database, and to evaluate performance of dynamic processing of RDBMS Database for identification of target vehicle and booster after separation. Produced experimentation results from improved performance of the proposed methodology on which futuristic command control system can rely.

Engineering Agile Big-Data Systems

To be effective, data-intensive systems require extensive ongoing customisation to reflect changing user requirements, organisational policies, and the structure and interpretation of the data they hold. Manual customisation is expensive, time-consuming, and error-prone. In large complex systems, the value of the data can be such that exhaustive testing is necessary before any new feature can be added to the existing design. In most cases, the precise details of requirements, policies and data will change during the lifetime of the system, forcing a choice between expensive modification and continued operation with an inefficient design.Engineering Agile Big-Data Systems outlines an approach to dealing with these problems in software and data engineering, describing a methodology for aligning these processes throughout product lifecycles. It discusses tools which can be used to achieve these goals, and, in a number of case studies, shows how the tools and methodology have been used to improve a variety of academic and business systems

Data exploration systems for databases

Data exploration systems apply machine learning techniques, multivariate statistical methods, information theory, and database theory to databases to identify significant relationships among the data and summarize information. The result of applying data exploration systems should be a better understanding of the structure of the data and a perspective of the data enabling an analyst to form hypotheses for interpreting the data. This paper argues that data exploration systems need a minimum amount of domain knowledge to guide both the statistical strategy and the interpretation of the resulting patterns discovered by these systems

Data warehouse stream view update with hash filter.

A data warehouse usually contains large amounts of information representing an integration of base data from one or more external data sources over a long period of time to provide fast-query response time. It stores materialized views which provide aggregation (SUM, MIX, MIN, COUNT and AVG) on some measure attributes of interest for data warehouse users. The process of updating materialized views in response to the modification of the base data is called materialized view maintenance. Some data warehouse application domains, like stock markets, credit cards, automated banking and web log domains depend on data sources updated as continuous streams of data. In particular, electronic stock trading markets such as the NASDAQ, generate large volumes of data, in bursts that are up to 4,200 messages per second. This thesis proposes a new view maintenance algorithm (StreamVup), which improves on semi join methods by using hash filters. The new algorithm first, reduce the amount of bytes transported through the network for streams tuples, and secondly reduces the cost of join operations during view update by eliminating the recompution of view updates caused by newly arriving duplicate tuples. (Abstract shortened by UMI.)Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2003 .I85. Source: Masters Abstracts International, Volume: 42-05, page: 1753. Adviser: C. I. Ezeife. Thesis (M.Sc.)--University of Windsor (Canada), 2003

Data warehouse stream view update with multiple streaming.

The main objective of data warehousing is to store information representing an integration of base data from single or multiple data sources over an extended period of time. To provide fast access to the data, regardless of the availability of the data source, data warehouses often use materialized views. Materialized views are able to provide aggregation on some attributes to help Decision Support Systems. Updating materialized views in response to modifications in the base data is called materialized view maintenance. In some applications, for example, the stock market and banking systems, the source data is updated so frequently that we can consider them as a continuous stream of data. To keep the materialized view updated with respect to changes in the base tables in a traditional way will cause query response times to increase. This thesis proposes a new view maintenance algorithm for multiple streaming which improves semi-join methods and hash filter methods. Our proposed algorithm is able to update a view which joins two base tables where both of the base tables are in the form of data streams (always changing). By using a timestamp, building updategrams in parallel and by optimizing the joining cost between two data sources it can reduce the query response time or execution time significantly.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .A336. Source: Masters Abstracts International, Volume: 44-03, page: 1391. Thesis (M.Sc.)--University of Windsor (Canada), 2005

A prevenção da violência por parceiro(a) íntimo(a) na adolescência: uma revisão integrativa

OBJETIVO Analisar a produção científica sobre a prevenção da violência por parceiro(a) íntimo(a) entre adolescentes no campo da saúde, considerando as categorias de gênero e geração. MÉTODO Revisão integrativa, cuja busca foi realizada nas bases de dados LILACS, PubMed/MEDLINE e SciELO. RESULTADOS Foram selecionados 30 artigos. Os resultados indicam que a maior parte dos estudos tratava da avaliação de intervenções por programas de prevenção da violência por parceiro(a) íntimo(a). O método predominante foi o quantitativo, no que concerne à área de conhecimento os estudos concentram-se nas áreas de enfermagem, psicologia e medicina. A maioria dos cenários dos estudos foi constituída por escolas, seguidos de domicílio, hospital, centro de saúde e tribo indígena. CONCLUSÃO Constata-se que a análise do fenômeno não foi realizada a partir de uma perspectiva de gênero e geração, estando a produção científica pautada nos moldes positivistas de pesquisa, que se aproximam da lógica da saúde pública clássica e centrada na dimensão singular.OBJETIVO Analizar la producción científica acerca de la prevención de la violencia por pareja íntima entre adolescentes en el campo de la salud, considerando las categorías de género y generación. MÉTODO Revisión integradora, cuya búsqueda se llevó a cabo en las bases de datos LILACS, PubMed/MEDLINE y SciELO. RESULTADOS Fueron seleccionados 30 artículos. Los resultados señalan que la mayor parte de los estudios trataba de la evaluación de intervenciones por programas de prevención de la violencia por pareja íntima. El método predominante fue el cuantitativo; en lo que se refiere al área de conocimiento los estudios se concentran en las áreas de enfermería, psicología y medicina. La mayoría de los escenarios de los estudios estuvo constituida de escuelas, seguidos de domicilio, hospital, centro de salud y tribu indígena. CONCLUSIÓN Se constata que el análisis del fenómeno no fue realizado desde una perspectiva de género y generación, estando la producción científica pautada en los modelos positivistas de investigación, que se acercan a la lógica de la salud pública clásica y centrada en la dimensión singular.OBJECTIVE To analyze the scientific literature on preventing intimate partner violence among adolescents in the field of health based on gender and generational categories. METHOD This was an integrative review. We searched for articles using LILACS, PubMed/MEDLINE, and SciELO databases. RESULTS Thirty articles were selected. The results indicate that most studies assessed interventions conducted by programs for intimate partner violence prevention. These studies adopted quantitative methods, and most were in the area of nursing, psychology, and medicine. Furthermore, most research contexts involved schools, followed by households, a hospital, a health center, and an indigenous tribe. CONCLUSION The analyses were not conducted from a gender- and generation-based perspective. Instead, the scientific literature was based on positivist research models, intimately connected to the classic public healthcare model and centered on a singular dimension

Accelerating Innovation Through Analogy Mining

The availability of large idea repositories (e.g., the U.S. patent database) could significantly accelerate innovation and discovery by providing people with inspiration from solutions to analogous problems. However, finding useful analogies in these large, messy, real-world repositories remains a persistent challenge for either human or automated methods. Previous approaches include costly hand-created databases that have high relational structure (e.g., predicate calculus representations) but are very sparse. Simpler machine-learning/information-retrieval similarity metrics can scale to large, natural-language datasets, but struggle to account for structural similarity, which is central to analogy. In this paper we explore the viability and value of learning simpler structural representations, specifically, "problem schemas", which specify the purpose of a product and the mechanisms by which it achieves that purpose. Our approach combines crowdsourcing and recurrent neural networks to extract purpose and mechanism vector representations from product descriptions. We demonstrate that these learned vectors allow us to find analogies with higher precision and recall than traditional information-retrieval methods. In an ideation experiment, analogies retrieved by our models significantly increased people's likelihood of generating creative ideas compared to analogies retrieved by traditional methods. Our results suggest a promising approach to enabling computational analogy at scale is to learn and leverage weaker structural representations.Comment: KDD 201