The Use of Olap Reporting Technology to Improve Patient Care Services Decision Making Within the University Health Center

The purpose of this paper is to demonstrate that it is feasible for the student health center to leverage existing clinical data in a data warehouse by using OLAP reporting in order to improve patient care and health care services decision making. Historically, University health care centers have relied mainly on operational data sources for critical health care decision making. These sources of data do not contain enough information to allow health officials to recognize trends or predict how future changes in health care services might vastly improve overall heath care. Four proof of concept artifacts are constructed through design science research methodology, and a feasibility study is presented to build the case for the adoption of OLAP reporting technology. The study concludes that it is feasible to implement an OLAP reporting infrastructure at the student health center if physicians, clinical staff, and administration clearly define the need for the new technology, develop proper data extraction, loading, and transformation strategy, and adequately provide project management and data warehouse design towards the implementation of the solution

Introducing Students to Business Intelligence: Acceptance and Perceptions of OLAP Software

El presente trabajo de investigación titula: “Influencia del Control Interno en la Autoridad Efectiva, de los Profesores y el Director, en los Colegios Emblemáticos de la Provincia de Arequipa, 2016” y como objetivo general tiene el propósito determinar la influencia del control interno en la autoridad efectiva, de los profesores y el director, en las instituciones educativas emblemáticas de la provincia de Arequipa, 2016; que se motiva en las interferencias intrusivas del ejercicio del control interno por parte de los profesores en la autoridad poco efectiva por parte de los directores. El método de la investigación es el hipotético deductivo; con diseño descriptivo explicativo, de corte transversal; cuya población está constituida por 316 unidades de estudio, de los que 11 son directores y 305 son profesores; para ello la muestra está conformada por 173, de los que 7 son directores y 166 son profesores; para tal efecto se aplicó la técnica de recolección de datos; encuesta para ambas variables; para la variable independiente el instrumento es la ficha cuestionario sobre el control interna, y para la variable dependiente la ficha cuestionario sobre la autoridad efectiva; para la contrastación de hipótesis se aplicó la X2 y el análisis de regresión. Se concluyó que en la asociación de variables, el 43.4 % está en un nivel La mayoría de veces en la variable control interno, y en el nivel Siempre en el caso de la autoridad efectiva; estadísticamente hay diferencia significativa (x2: Sig. Asintótica= 0.000) y ANOVA 0.000; por lo que se verificó la hipótesis general, en el sentido que: El control interno influye negativa y significativamente en la autoridad efectiva de los profesores y el director, en las Instituciones Educativas Emblemáticas de la provincia de Arequipa, año 2016.Tesi

CubiST++: Evaluating Ad-Hoc CUBE Queries Using Statistics Trees

We report on a new, efficient encoding for the data cube, which results in a drastic speed-up of OLAP queries that aggregate along any combination of dimensions over numerical and categorical attributes. We are focusing on a class of queries called cube queries, which return aggregated values rather than sets of tuples. Our approach, termed CubiST++ (Cubing with Statistics Trees Plus Families), represents a drastic departure from existing relational (ROLAP) and multi-dimensional (MOLAP) approaches in that it does not use the view lattice to compute and materialize new views from existing views in some heuristic fashion. Instead, CubiST++ encodes all possible aggregate views in the leaves of a new data structure called statistics tree (ST) during a one-time scan of the detailed data. In order to optimize the queries involving constraints on hierarchy levels of the underlying dimensions, we select and materialize a family of candidate trees, which represent superviews over the different hierarchical levels of the dimensions. Given a query, our query evaluation algorithm selects the smallest tree in the family, which can provide the answer. Extensive evaluations of our prototype implementation have demonstrated its superior run-time performance and scalability when compared with existing MOLAP and ROLAP systems

Reordering Rows for Better Compression: Beyond the Lexicographic Order

Sorting database tables before compressing them improves the compression rate. Can we do better than the lexicographical order? For minimizing the number of runs in a run-length encoding compression scheme, the best approaches to row-ordering are derived from traveling salesman heuristics, although there is a significant trade-off between running time and compression. A new heuristic, Multiple Lists, which is a variant on Nearest Neighbor that trades off compression for a major running-time speedup, is a good option for very large tables. However, for some compression schemes, it is more important to generate long runs rather than few runs. For this case, another novel heuristic, Vortex, is promising. We find that we can improve run-length encoding up to a factor of 3 whereas we can improve prefix coding by up to 80%: these gains are on top of the gains due to lexicographically sorting the table. We prove that the new row reordering is optimal (within 10%) at minimizing the runs of identical values within columns, in a few cases.Comment: to appear in ACM TOD

Making the Case for a Business Intelligence Framework

This research is intended to develop evidence for whether or not large organizations should spend a large amount of time and resources on building Business Intelligence Frameworks by examining Project Manager’s perceptions of complex information systems. Project Managers in a large organization provide a cross functional reporting role that requires them to delve into information technology systems in complex ways when querying for simple metrics related to projects they manage. Using an online survey, this study found that project manager’s perceptions changed more positively towards IT systems performing automatic queries, web based queries, IT systems, and business intelligence system dashboards if they did not already have a business intelligence framework in place, and if they were less experienced. More experienced project managers had lower perceptions of current IT systems, automatic queries, web-based queries, and dashboards. There is evidence to suggest that business intelligence frameworks will be positively perceived for project managers with lower experience, and where these systems have not already been introduced

A UML profile for multidimensional modeling in data warehouses

The multidimensional (MD) modeling, which is the foundation of data warehouses (DWs), MD databases, and On-Line Analytical Processing (OLAP) applications, is based on several properties different from those in traditional database modeling. In the past few years, there have been some proposals, providing their own formal and graphical notations, for representing the main MD properties at the conceptual level. However, unfortunately none of them has been accepted as a standard for conceptual MD modeling. In this paper, we present an extension of the Unified Modeling Language (UML) using a UML profile. This profile is defined by a set of stereotypes, constraints and tagged values to elegantly represent main MD properties at the conceptual level. We make use of the Object Constraint Language (OCL) to specify the constraints attached to the defined stereotypes, thereby avoiding an arbitrary use of these stereotypes. We have based our proposal in UML for two main reasons: (i) UML is a well known standard modeling language known by most database designers, thereby designers can avoid learning a new notation, and (ii) UML can be easily extended so that it can be tailored for a specific domain with concrete peculiarities such as the multidimensional modeling for data warehouses. Moreover, our proposal is Model Driven Architecture (MDA) compliant and we use the Query View Transformation (QVT) approach for an automatic generation of the implementation in a target platform. Throughout the paper, we will describe how to easily accomplish the MD modeling of DWs at the conceptual level. Finally, we show how to use our extension in Rational Rose for MD modeling.This work has been partially supported by the METASIGN project (TIN2004-00779) from the Spanish Ministry of Education and Science, by the DADASMECA project (GV05/220) from the Regional Government of Valencia, and by the MESSENGER (PCC-03-003-1) and DADS (PBC-05-012-2) projects from the Regional Science and Technology Ministry of Castilla-La Mancha (Spain)

Uma interface baseada em conhecimento para interação com data warehouses espaciais

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-graduação em Ciência da Computação, Florianópolis, 2010A análise de informação em um data warehouse espacial (SDW) pode envolver o manuseio de grandes volumes de dados espaciais. Usuários de domínios específicos de aplicação, com habilidades básicas de computação, são geralmente incapazes ou têm sérias dificuldades para resolver suas necessidades de análise de informação interagindo diretamente com SDWs, embora alguns sejam capazes de interagir com data warehouses (DW) convencionais através de uma interface gráfica (GUI). As dificuldades são maiores em um SDW que em um DW convencional, entre outras razões, pela variedade e complexidade dos dados espaciais, operadores espaciais e funções de agregação espacial utilizadas para especificar consultas SOLAP. Este trabalho propõe um sistema baseado em conhecimento, chamado de S2DW (Semantic and Spatial Data Warehouses), para auxiliar estes usuários de domínios específicos a efetuar análises de informação em SDWs, acessando descrições semânticas dos data marts espaciais através de uma interface gráfica baseada em conhecimento (GUI). Este trabalho descreve a arquitetura geral do S2DW e foca em sua GUI. A interface gráfica baseada em conhecimento do S2DW permite ao usuário pesquisar data marts relacionados a um determinado assunto, através da especificação de palavras-chave ou pela navegação em uma visão de uma ontologia do domínio. Cada data mart relacionado ao assunto pesquisado é apresentado ao usuário como um grafo representando a estrutura dimensional do cubo de informação. Este grafo é semanticamente enriquecido com descrições do conteúdo dos dados e dos recursos de processamento de dados do data mart espacial. Consultas espaciais OLAP podem ser especificadas interagindo com a interface gráfica baseada em conhecimento, a qual orienta o usuário a compor adequadamente operadores e funções para tratar os diferentes tipos de dados disponíveis no data mart, visando atender diferentes necessidades de análise. As tabelas, gráficos e mapas fornecidos como resposta as consultas SOLAP também permitem a interação do usuário para gradualmente refinar a análise da informação. As principais contribuições deste trabalho são a proposta inicial da GUI baseada em conhecimento do S2DW e o teste de usabilidade desta GUI, em um estudo de caso com usuários reais do domínio agrícola

Business Intelligence on Non-Conventional Data

The revolution in digital communications witnessed over the last decade had a significant impact on the world of Business Intelligence (BI). In the big data era, the amount and diversity of data that can be collected and analyzed for the decision-making process transcends the restricted and structured set of internal data that BI systems are conventionally limited to. This thesis investigates the unique challenges imposed by three specific categories of non-conventional data: social data, linked data and schemaless data. Social data comprises the user-generated contents published through websites and social media, which can provide a fresh and timely perception about people’s tastes and opinions. In Social BI (SBI), the analysis focuses on topics, meant as specific concepts of interest within the subject area. In this context, this thesis proposes meta-star, an alternative strategy to the traditional star-schema for modeling hierarchies of topics to enable OLAP analyses. The thesis also presents an architectural framework of a real SBI project and a cross-disciplinary benchmark for SBI. Linked data employ the Resource Description Framework (RDF) to provide a public network of interlinked, structured, cross-domain knowledge. In this context, this thesis proposes an interactive and collaborative approach to build aggregation hierarchies from linked data. Schemaless data refers to the storage of data in NoSQL databases that do not force a predefined schema, but let database instances embed their own local schemata. In this context, this thesis proposes an approach to determine the schema profile of a document-based database; the goal is to facilitate users in a schema-on-read analysis process by understanding the rules that drove the usage of the different schemata. A final and complementary contribution of this thesis is an innovative technique in the field of recommendation systems to overcome user disorientation in the analysis of a large and heterogeneous wealth of data

Towards Data Governance for International Dementia Care Mapping (DCM). A Study Proposing DCM Data Management through a Data Warehousing Approach.

Information Technology (IT) plays a vital role in improving health care systems by enhancing the quality, efficiency, safety, security, collaboration and informing decision making. Dementia, a decline in mental ability which affects memory, concentration and perception, is a key issue in health and social care, given the current context of an aging population. The quality of dementia care is noted as an international area of concern. Dementia Care Mapping (DCM) is a systematic observational framework for assessing and improving dementia care quality. DCM has been used as both a research and practice development tool internationally. However, despite the success of DCM and the annual generation of a huge amount of data on dementia care quality, it lacks a governance framework, based on modern IT solutions for data management, such a framework would provide the organisations using DCM a systematic way of storing, retrieving and comparing data over time, to monitor progress or trends in care quality. Data Governance (DG) refers to the implications of policies and accountabilities to data management in an organisation. The data management procedure includes availability, usability, quality, integrity, and security of the organisation data according to their users and requirements. This novel multidisciplinary study proposes a comprehensive solution for governing the DCM data by introducing a data management framework based on a data warehousing approach. Original contributions have been made through the design and development of a data management framework, describing the DCM international database design and DCM data warehouse architecture. These data repositories will provide the acquisition and storage solutions for DCM data. The designed DCM data warehouse facilitates various analytical applications to be applied for multidimensional analysis. Different queries are applied to demonstrate the DCM data warehouse functionality. A case study is also presented to explain the clustering technique applied to the DCM data. The performance of the DCM data governance framework is demonstrated in this case study related to data clustering results. Results are encouraging and open up discussion for further analysis

Leveraging query logs for user-centric OLAP

OLAP (On-Line Analytical Processing), the process of efficiently enabling common analytical operations on the multidimensional view of data, is a corner stone of Business Intelligence.While OLAP is now a mature, efficiently implemented technology, very little attention has been paid to the effectiveness of the analysis and the user-friendliness of this technology, often considered tedious of use.This dissertation is a contribution to developing user-centric OLAP, focusing on the use of former queries logged by an OLAP server to enhance subsequent analyses. It shows how logs of OLAP queries can be modeled, constructed, manipulated, compared, and finally leveraged for personalization and recommendation.Logs are modeled as sets of analytical sessions, sessions being modeled as sequences of OLAP queries. Three main approaches are presented for modeling queries: as unevaluated collections of fragments (e.g., group by sets, sets of selection predicates, sets of measures), as sets of references obtained by partially evaluating the query over dimensions, or as query answers. Such logs can be constructed even from sets of SQL query expressions, by translating these expressions into a multidimensional algebra, and bridging the translations to detect analytical sessions. Logs can be searched, filtered, compared, combined, modified and summarized with a language inspired by the relational algebra and parametrized by binary relations over sessions. In particular, these relations can be specialization relations or based on similarity measures tailored for OLAP queries and analytical sessions. Logs can be mined for various hidden knowledge, that, depending on the query model used, accurately represents the user behavior extracted.This knowledge includes simple preferences, navigational habits and discoveries made during former explorations,and can be it used in various query personalization or query recommendation approaches.Such approaches vary in terms of formulation effort, proactiveness, prescriptiveness and expressive power:query personalization, i.e., coping with a current query too few or too many results, can use dedicated operators for expressing preferences, or be based on query expansion;query recommendation, i.e., suggesting queries to pursue an analytical session,can be based on information extracted from the current state of the database and the query, or be purely history based, i.e., leveraging the query log.While they can be immediately integrated into a complete architecture for User-Centric Query Answering in data warehouses, the models and approaches introduced in this dissertation can also be seen as a starting point for assessing the effectiveness of analytical sessions, with the ultimate goal to enhance the overall decision making process