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)

Constructing data marts from web sources using a graph common model

At a time when humans and devices are generating more information than ever, activities such as data mining and machine learning become crucial. These activities enable us to understand and interpret the information we have and predict, or better prepare ourselves for, future events. However, activities such as data mining cannot be performed without a layer of data management to clean, integrate, process and make available the necessary datasets. To that extent, large and costly data flow processes such as Extract-Transform-Load are necessary to extract from disparate information sources to generate ready-for-analyses datasets. These datasets are generally in the form of multi-dimensional cubes from which different data views can be extracted for the purpose of different analyses. The process of creating a multi-dimensional cube from integrated data sources is significant. In this research, we present a methodology to generate these cubes automatically or in some cases, close to automatic, requiring very little user interaction. A construct called a StarGraph acts as a canonical model for our system, to which imported data sources are transformed. An ontology-driven process controls the integration of StarGraph schemas and simple OLAP style functions generate the cubes or datasets. An extensive evaluation is carried out using a large number of agri data sources with user-defined case studies to identify sources for integration and the types of analyses required for the final data cubes

Automating the multidimensional design of data warehouses

Les experiències prèvies en l'àmbit dels magatzems de dades (o data warehouse), mostren que l'esquema multidimensional del data warehouse ha de ser fruit d'un enfocament híbrid; això és, una proposta que consideri tant els requeriments d'usuari com les fonts de dades durant el procés de disseny.Com a qualsevol altre sistema, els requeriments són necessaris per garantir que el sistema desenvolupat satisfà les necessitats de l'usuari. A més, essent aquest un procés de reenginyeria, les fonts de dades s'han de tenir en compte per: (i) garantir que el magatzem de dades resultant pot ésser poblat amb dades de l'organització, i, a més, (ii) descobrir capacitats d'anàlisis no evidents o no conegudes per l'usuari.Actualment, a la literatura s'han presentat diversos mètodes per donar suport al procés de modelatge del magatzem de dades. No obstant això, les propostes basades en un anàlisi dels requeriments assumeixen que aquestos són exhaustius, i no consideren que pot haver-hi informació rellevant amagada a les fonts de dades. Contràriament, les propostes basades en un anàlisi exhaustiu de les fonts de dades maximitzen aquest enfocament, i proposen tot el coneixement multidimensional que es pot derivar des de les fonts de dades i, conseqüentment, generen massa resultats. En aquest escenari, l'automatització del disseny del magatzem de dades és essencial per evitar que tot el pes de la tasca recaigui en el dissenyador (d'aquesta forma, no hem de confiar únicament en la seva habilitat i coneixement per aplicar el mètode de disseny elegit). A més, l'automatització de la tasca allibera al dissenyador del sempre complex i costós anàlisi de les fonts de dades (que pot arribar a ser inviable per grans fonts de dades).Avui dia, els mètodes automatitzables analitzen en detall les fonts de dades i passen per alt els requeriments. En canvi, els mètodes basats en l'anàlisi dels requeriments no consideren l'automatització del procés, ja que treballen amb requeriments expressats en llenguatges d'alt nivell que un ordenador no pot manegar. Aquesta mateixa situació es dona en els mètodes híbrids actual, que proposen un enfocament seqüencial, on l'anàlisi de les dades es complementa amb l'anàlisi dels requeriments, ja que totes dues tasques pateixen els mateixos problemes que els enfocament purs.En aquesta tesi proposem dos mètodes per donar suport a la tasca de modelatge del magatzem de dades: MDBE (Multidimensional Design Based on Examples) and AMDO (Automating the Multidimensional Design from Ontologies). Totes dues consideren els requeriments i les fonts de dades per portar a terme la tasca de modelatge i a més, van ser pensades per superar les limitacions dels enfocaments actuals.1. MDBE segueix un enfocament clàssic, en el que els requeriments d'usuari són coneguts d'avantmà. Aquest mètode es beneficia del coneixement capturat a les fonts de dades, però guia el procés des dels requeriments i, conseqüentment, és capaç de treballar sobre fonts de dades semànticament pobres. És a dir, explotant el fet que amb uns requeriments de qualitat, podem superar els inconvenients de disposar de fonts de dades que no capturen apropiadament el nostre domini de treball.2. A diferència d'MDBE, AMDO assumeix un escenari on es disposa de fonts de dades semànticament riques. Per aquest motiu, dirigeix el procés de modelatge des de les fonts de dades, i empra els requeriments per donar forma i adaptar els resultats generats a les necessitats de l'usuari. En aquest context, a diferència de l'anterior, unes fonts de dades semànticament riques esmorteeixen el fet de no tenir clars els requeriments d'usuari d'avantmà.Cal notar que els nostres mètodes estableixen un marc de treball combinat que es pot emprar per decidir, donat un escenari concret, quin enfocament és més adient. Per exemple, no es pot seguir el mateix enfocament en un escenari on els requeriments són ben coneguts d'avantmà i en un escenari on aquestos encara no estan clars (un cas recorrent d'aquesta situació és quan l'usuari no té clares les capacitats d'anàlisi del seu propi sistema). De fet, disposar d'uns bons requeriments d'avantmà esmorteeix la necessitat de disposar de fonts de dades semànticament riques, mentre que a l'inversa, si disposem de fonts de dades que capturen adequadament el nostre domini de treball, els requeriments no són necessaris d'avantmà. Per aquests motius, en aquesta tesi aportem un marc de treball combinat que cobreix tots els possibles escenaris que podem trobar durant la tasca de modelatge del magatzem de dades.Previous experiences in the data warehouse field have shown that the data warehouse multidimensional conceptual schema must be derived from a hybrid approach: i.e., by considering both the end-user requirements and the data sources, as first-class citizens. Like in any other system, requirements guarantee that the system devised meets the end-user necessities. In addition, since the data warehouse design task is a reengineering process, it must consider the underlying data sources of the organization: (i) to guarantee that the data warehouse must be populated from data available within the organization, and (ii) to allow the end-user discover unknown additional analysis capabilities.Currently, several methods for supporting the data warehouse modeling task have been provided. However, they suffer from some significant drawbacks. In short, requirement-driven approaches assume that requirements are exhaustive (and therefore, do not consider the data sources to contain alternative interesting evidences of analysis), whereas data-driven approaches (i.e., those leading the design task from a thorough analysis of the data sources) rely on discovering as much multidimensional knowledge as possible from the data sources. As a consequence, data-driven approaches generate too many results, which mislead the user. Furthermore, the design task automation is essential in this scenario, as it removes the dependency on an expert's ability to properly apply the method chosen, and the need to analyze the data sources, which is a tedious and timeconsuming task (which can be unfeasible when working with large databases). In this sense, current automatable methods follow a data-driven approach, whereas current requirement-driven approaches overlook the process automation, since they tend to work with requirements at a high level of abstraction. Indeed, this scenario is repeated regarding data-driven and requirement-driven stages within current hybrid approaches, which suffer from the same drawbacks than pure data-driven or requirement-driven approaches.In this thesis we introduce two different approaches for automating the multidimensional design of the data warehouse: MDBE (Multidimensional Design Based on Examples) and AMDO (Automating the Multidimensional Design from Ontologies). Both approaches were devised to overcome the limitations from which current approaches suffer. Importantly, our approaches consider opposite initial assumptions, but both consider the end-user requirements and the data sources as first-class citizens.1. MDBE follows a classical approach, in which the end-user requirements are well-known beforehand. This approach benefits from the knowledge captured in the data sources, but guides the design task according to requirements and consequently, it is able to work and handle semantically poorer data sources. In other words, providing high-quality end-user requirements, we can guide the process from the knowledge they contain, and overcome the fact of disposing of bad quality (from a semantical point of view) data sources.2. AMDO, as counterpart, assumes a scenario in which the data sources available are semantically richer. Thus, the approach proposed is guided by a thorough analysis of the data sources, which is properly adapted to shape the output result according to the end-user requirements. In this context, disposing of high-quality data sources, we can overcome the fact of lacking of expressive end-user requirements.Importantly, our methods establish a combined and comprehensive framework that can be used to decide, according to the inputs provided in each scenario, which is the best approach to follow. For example, we cannot follow the same approach in a scenario where the end-user requirements are clear and well-known, and in a scenario in which the end-user requirements are not evident or cannot be easily elicited (e.g., this may happen when the users are not aware of the analysis capabilities of their own sources). Interestingly, the need to dispose of requirements beforehand is smoothed by the fact of having semantically rich data sources. In lack of that, requirements gain relevance to extract the multidimensional knowledge from the sources.So that, we claim to provide two approaches whose combination turns up to be exhaustive with regard to the scenarios discussed in the literaturePostprint (published version

Automating data mart construction from semi-structured data sources

The global food and agricultural industry has a total market value of USD 8 trillion in 2016, and decision makers in the Agri sector require appropriate tools and up-to-date information to make predictions across a range of products and areas. Traditionally, these requirements are met with information processed into a data warehouse and data marts constructed for analyses. Increasingly however, data is coming from outside the enterprise and often in unprocessed forms. As these sources are outside the control of companies, they are prone to change and new sources may appear. In these cases, the process of accommodating these sources can be costly and very time consuming. To automate this process, what is required is a sufficiently robust Extract-Transform-Load (ETL) process; external sources are mapped to some form of ontology, and an integration process to merge the specific data sources. In this paper, we present an approach to automating the integration of data sources in an Agri environment, where new sources are examined before an attempt to merge them with existing data marts. Our validation uses three separate case studies of real world data to demonstrate the robustness of our approach and the efficiency of materialising data mart

A Strategy for Reducing I/O and Improving Query Processing Time in an Oracle Data Warehouse Environment

In the current information age as the saying goes, time is money. For the modern information worker, decisions must often be made quickly. Every extra minute spent waiting for critical data could mean the difference between financial gain and financial ruin. Despite the importance of timely data retrieval, many organizations lack even a basic strategy for improving the performance of their data warehouse based reporting systems. This project explores the idea that a strategy making use of three database performance improvement techniques can reduce I/O (input/output operations) and improve query processing time in an information system designed for reporting. To demonstrate that these performance improvement goals can be achieved, queries were run on ordinary tables and then on tables utilizing the performance improvement techniques. The I/O statistics and processing times for the queries were compared to measure the amount of performance improvement. The measurements were also used to explain how these techniques may be more or less effective under certain circumstances, such as when a particular type of query is run. The collected I/O and time based measurements showed a varying degree of improvement for each technique based on the query used. A need to match the types of queries commonly run on the system to the performance improvement technique being implemented was found to be an important consideration. The results indicated that in a reporting environment these performance improvement techniques have the potential to reduce I/O and improve query performance

Ontology based data warehousing for mining of heterogeneous and multidimensional data sources

Heterogeneous and multidimensional big-data sources are virtually prevalent in all business environments. System and data analysts are unable to fast-track and access big-data sources. A robust and versatile data warehousing system is developed, integrating domain ontologies from multidimensional data sources. For example, petroleum digital ecosystems and digital oil field solutions, derived from big-data petroleum (information) systems, are in increasing demand in multibillion dollar resource businesses worldwide. This work is recognized by Industrial Electronic Society of IEEE and appeared in more than 50 international conference proceedings and journals

Analytical study and computational modeling of statistical methods for data mining

Today, there is tremendous increase of the information available on electronic form. Day by day it is increasing massively. There are enough opportunities for research to retrieve knowledge from the data available in this information. Data mining and app

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