On-Line Analytical Processing Tools Research Articles

Beyond TPC-DS, a benchmark for Big Data OLAP systems (BDOLAP-Bench)

Online Analytical Processing (OLAP) systems with Big Data support allow storing tables of up to tens of billions of rows or terabytes of data. At the same time, these tools allow the execution of analytical queries with interactive response times, thus making them suitable for the implementation of Business Intelligence applications. However, since there can be significant differences in query and data loading performance between current Big Data OLAP tools, it is worthwhile to evaluate and compare them using a benchmark. But we identified that none of the existing approaches are really suitable for this type of system. To address this, in this research we propose a new benchmark specifically designed for Big Data OLAP systems and based on the widely adopted TPC-DS benchmark. To overcome TPC-DS inadequacy, we propose (i) a set of transformations to support the implementation of its sales data mart on any current Big Data OLAP system, (ii) a choice of 16 genuine OLAP queries, and (iii) an improved data maintenance performance metric. Moreover, we validated our benchmark through its implementation on four representative systems.

Building a Lung and Ovarian Cancer Data Warehouse.

ObjectivesDespite the collection of vast amounts of data by the healthcare sector, effective decision-making in medical practice is still challenging. Data warehousing technology can be applied for the collection and management of clinical data from various sources to provide meaningful insights for physicians and administrators. Cancer data are extremely complicated and massive; hence, a clinical data warehouse system can provide insights into prevention, diagnosis and treatment processes through the use of online analytical processing tools for the analysis of multi-dimensional data at different granularity levels.MethodsIn this study, a clinical data warehouse was developed for lung cancer data, which were kindly provided by the United States National Cancer Institute. Lung and ovarian cancer data were imported in specific formats and cleaned to remove errors and redundancies. SQL server integration services (SSIS) were used for the extract-transform-load (ETL) process.ResultsThe design of the clinical data warehouse responds efficiently to all types of queries by adopting the fact constellation schema model. Various online analytical processing queries can be expressed using the proposed approach.ConclusionsThis model succeeded in responding to complex queries, and the analysis of data is facilitated by using online analytical processing cubes and viewing multilevel data details.

Long-term prognostic counselling in people with multiple sclerosis using an online analytical processing tool

Background: Prognostic counselling is a sensitive issue in medicine and especially so in MS due to the highly heterogeneous disease course. However, people with MS (pwMS) seek prognostic information. The web-based ‘Evidence-Based Decision Support Tool in Multiple Sclerosis’ (EBDiMS) uses data of 717 patients from the London/Ontario cohort to calculate personalized long-term prognostic information. Objective: The aim of this study was to investigate the feasibility and effect of long-term prognostic counselling in pwMS using EBDiMS. Methods: Ninety consecutive pwMS were provided with personalized estimations of expected time to reach Expanded Disability Status Scale (EDSS) scores of 6 and 8 and time to conversion to secondary-progressive MS. Participants gave estimates on their own putative prognosis and rated the tool’s acceptability on six-step Likert-type scales. Results: Participants rated EBDiMS as highly understandable, interesting and relevant for patient–physician encounters, coping and therapy decisions. Although it provoked a certain degree of worry in some participants, 95% would recommend using the tool. Participants’ own prognosis estimates did not change significantly following EBDiMS. Conclusion: Long-term prognostic counselling using an online tool has been shown to be feasible in a clinical setting. EBDiMS provides pwMS with relevant, easy-to-understand, long-term prognostic information without causing relevant anxiety.

In silico functional and structural characterization of hepatitis B virus PreS/S-gene in Iranian patients infected with chronic hepatitis B virus genotype D

ObjectiveChronic hepatitis B (CHB) virus infection is the most prevalent chronic liver disease and has become a serious threat to human health. In this study, we attempted to specify and predict several properties including physicochemical, mutation sites, B-cell epitopes, phosphorylation sites, N-link, O-link glycosylation sites, and protein structures of S protein isolated from Ahvaz. Materials and methodsInitially, hepatitis B virus DNA (HBV DNA) was extracted from five sera samples of untreated chronic hepatitis B patients. The full-length HBV genomes were amplified and then cloned in pTZ57 R/T vector. The full sequences of HBV were registered in the GenBank with accessions numbers (MK355500), (MK355501) and (MK693107-9). PROTSCALE, Expasy's ProtParam, immuneepitope, ABCpred, BcePred, Bepipred, Algpred, VaxiJen, SCRATCH, DiANNA, plus a number of online analytical processing tools were used to analyse and predict the preS/S gene of genotype D sequences. The present study is the first analytical research on samples obtained from Ahvaz. ResultsWe found major hydrophilic region (MHR) mutations at “a” determining region that included K122R, N131T, F134Y, P142L, and T126N mutations. Moreover, Ahvaz sequences revealed four sites (4, 112, 166, and 309) in the preS/S gene for N-glycosylation that could possibly be a potential target for anti-HBV therapy. ConclusionIn the present study, mutations were identified at positions T113S and N131T within the MHR region of S protein; these mutations can potentially decrease the effect of hepatitis B vaccination in vaccine recipients.

DataWarehouse Design Acceptance of Goods In PT Transmart Using Tools Pentaho and Tableu

The purpose of this research is to process the data of receipt of goods at PT Transmart Central Park, analyze the existing database in the information system of receipt of goods to get the necessary information and design the data warehouse to integrate the existing data so that obtained information to take a decision. Business Intelligence (BI) is one of information technology that can be solution to collect, store and analyze company data. Online Analytical Processing (OLAP) consists of a set of tools in use to help the process of analysis and comparison of data in the database. OLAP tools and methods help users analyze data in a data warehouse by providing a variety of dynamic graphical display data. The data warehouse design method is implemented by applying the 9 steps (Nine-Step Methodology) used by Ralph Kimball in designing the star scheme. The results achieved are data warehouse that provides the desired information and provide summary information in the form of tables and charts, can be compared between the purchase order data with receiving report is global, relevant, and integrated that can be seen from various points of view so useful for the leaders to make a decision.Keywords : Bussinesn Intelligent,Datawarehouse,OlapNine-Step Methodology

The Development of Data Warehouse from Payment Point Services of IT Business Solution Provider

The goals of the research is to develop a data warehouse loaded from operational database of payment point service in IT business solution provider. The development scope is data analysis using Online Analytical Processing (OLAP) tools for identifying the trends of transaction and agent registration, and create reports and dashboards. The data warehouse was developed with Kimball method as known as the nine-step design methodology. The data and requirement were collected by observation and interview with Chief Technology Officer (CTO). The data warehouse was analyzed by OLAP tool provided by Pentaho Business Analytics software with additional plugin Pivot4J. The obtained results are the trends of transactionand agent registration between 2014 and 2015. It can be concluded that developed data warehouse can be used as ananalysis tool to know the trends information.

Information gems from criminal mines

Purpose The purpose of this study is to demonstrate how existing volumes of big city crime data could be converted to significantly useful information by law enforcement agencies using readily available data warehouse and OLAP technologies. During the post-9/11 era, criminal data collection by law enforcement agencies received significant attention across the world. Rapid advancement of technology helped collection and storage of these data in large volumes, but often do not get analyzed due to improper data format, lack of technological knowledge and time. Data warehousing (DW) and On-line Analytical Processing (OLAP) tools can be used to organize and present these data in a form strategically meaningful to the general public. In this study, the authors took a seven-month sample crime data from the City of Houston Police Department’s website, cleaned and organized them into a data warehouse with the hope of answering common questions related to crime statistics in a big city in the USA. Design/methodology/approach The raw data for the seven-month period was collected from the website in Microsoft Excel spreadsheet format for each month. The data were then cleaned, described, renamed, formatted and then imported into a compiled Access database along with the definition of Facts and Dimensions using a STAR Schema. Data were then transferred to the Microsoft SQL Server data warehouse. SQL Server Analysis Services and Visual Studio Business Intelligent Tool are used to create a Data Cube for OLAP analysis of the summarized data. Findings To prove the usefulness of the DW and OLAP cube, the authors have shown few sample queries displaying the number and the types of crimes as a function of time of the day, location, premises, etc. For example, the authors found that 98 crimes occurred on a major street in the city during the early working hours (7 am and 12 pm) when nobody virtually was at home, and among those crimes, roughly two-thirds of them are thefts. This summarized information is significantly useful to the general public and the law enforcement agencies. Research limitations/implications The authors’ research is limited to one city’s crime data, whose data set might be different from other cities. In addition to the volume of data and lack of descriptions, the major limitations encountered were the lack of major neighborhood names and their relation to streets. There are other government agencies that provide data to this effect, and a standard set of data would facilitate the process. The authors also looked at data for a nine-month period only. Analyzing data over many years will provide time-trend of crime statistics for a longer period of time. Practical implications Many federal, state and local law enforcement agencies are rapidly embracing technology to publish crime data through their websites. However, more attention will need to be paid to the quality and utility of this information to the general public. At the time, there exists no compiled source of crime data or its trend as a function of time, crime type, location and premises. There needs to be a coherent system that allows for an average citizen to obtain this information in a more consumable package. DW and OLAP tools can provide this information package. Social implications Having the crime data of a big city in a consumable form is immensely useful for all segments of the constituency that the government agencies serve and will become a service that these offices will be expected to deliver on demand. This information could also be useful in many instances for the decision makers, ranging from those seeking to start a business, to those seeking a place to live who may not necessarily know which neighborhoods or parts of the city are more prone to criminal activity than others. Originality/value While there have been few reports of possible use of DW and OALP technologies to study criminal data, the authors found that not many authors used actual crime data, the data sets and formats used in each case are different, results are not presented in most cases and the actual vendor technologies implemented can be different as well. In this paper, the authors present how DW and OLAP tools readily available in most enterprises can be used to analyze publicly available criminal datasets and convert them into meaningful information, which can be valuable not only to the law enforcement agencies but to the public at large.

Multidimensional analysis model for a document warehouse that includes textual measures

Data warehouses and On-Line Analytical Processing tools, OLAP, together permit a multi-dimensional analysis of structured data information. However, as business systems are increasingly required to handle substantial quantities of unstructured textual information, the need arises for an effective and similar means of analysis. To manage unstructured text data stored in data warehouses, a new multi-dimensional analysis model is proposed that includes textual measures as well as a topic hierarchy. In this model, the textual measures that associate the topics with the text documents are generated by Probabilistic Latent Semantic Analysis, while the hierarchy is created automatically using a clustering algorithm. Documents are then able to be queried using OLAP tools. The model was evaluated from two viewpoints — query execution time and user satisfaction. Evaluation of execution time was carried out on scientific articles using two query types and user satisfaction (with query time and ease of use) using statistical frequency and multivariate analyses. Encouraging observations included that as the number of documents increases, query time increases as a lineal, rather than exponential tendency. In addition, the model gained an increasing acceptance with use, while the visualization of the model was also well received by users.

An MDA approach for developing secure OLAP applications: Metamodels and transformations

Decision makers query enterprise information stored in Data Warehouses (DW) by using tools (such as On-Line Analytical Processing (OLAP) tools) which employ specific views or cubes from the corporate DW or Data Marts, based on multidimensional modelling. Since the information managed is critical, security constraints have to be correctly established in order to avoid unauthorized access. In previous work we defined a Model-Driven based approach for developing a secure DW repository by following a relational approach. Nevertheless, it is also important to define security constraints in the metadata layer that connects the DW repository with the OLAP tools; that is, over the same multidimensional structures that end users manage. This paper incorporates a proposal for developing secure OLAP applications within our previous approach: it improves a UML profile for conceptual modelling; it defines a logical metamodel for OLAP applications; and it defines and implements transformations from conceptual to logical models, as well as from logical models to secure implementation in a specific OLAP tool (SQL Server Analysis Services).

An architecture for automatically developing secure OLAP applications from models

ContextDecision makers query enterprise information stored in Data Warehouses (DW) by using tools (such as On-Line Analytical Processing (OLAP) tools) which use specific views or cubes from the corporate DW or Data Marts, based on the multidimensional modeling. Since the information managed is critical, security constraints have to be correctly established in order to avoid unauthorized accesses. ObjectiveIn previous work we have defined a Model-Driven based approach for developing a secure DWs repository by following a relational approach. Nevertheless, is also important to define security constraints in the metadata layer that connects the DWs repository with the OLAP tools, that is, over the same multidimensional structures that final users manage. This paper defines a proposal to develop secure OLAP applications and incorporates it into our previous approach. MethodOur proposal is composed of models and transformations. Our models have been defined using the extension capabilities from UML (conceptual model) and extending the OLAP package of CWM with security (logical model). Transformations have been defined by using a graphical notation and implemented into QVT and MOFScript. Finally, this proposal has been evaluated through case studies. ResultsA complete MDA architecture for developing secure OLAP applications. The main contributions of this paper are: improvement of a UML profile for conceptual modeling; definition of a logical metamodel for OLAP applications; and definition and implementation of transformations from conceptual to logical models, and from logical models to the secure implementation into a specific OLAP tool (SSAS). ConclusionOur proposal allows us to develop secure OLAP applications, providing a complete MDA architecture composed of several security models and automatic transformations towards the final secure implementation. Security aspects are early identified and fitted into a most robust solution that provides us a better information assurance and a saving of time in maintenance.

Multidimensional modeling and analysis of large and complex watercourse data: an OLAP-based solution

This paper presents the application of Data Warehouse (DW) and On-Line Analytical Processing (OLAP) technologies to the field of water quality assessment. The European Water Framework Directive (DCE, 2000) underlined the necessity of having operational tools to help in the interpretation of the complex and abundant information regarding running waters and their functioning. Several studies have exemplified the interest in DWs for integrating large volumes of data and in OLAP tools for data exploration and analysis.Based on free software tools, we propose an extensible relational OLAP system for the analysis of physicochemical and hydrobiological watercourse data. This system includes: (i) two data cubes; (ii) an Extract, Transform and Load (ETL) tool for data integration; and (iii) tools for OLAP exploration. Many examples of OLAP analysis (thematic, temporal, spatiotemporal, and multiscale) are provided. We have extended an existing framework with complex aggregate functions that are used to define complex analysis indicators. Additional analysis dimensions are also introduced to allow their calculation and also for purposes of rendering information. Finally, we propose two strategies to address the problem of summarizing heterogeneous measurement units by: (i) transforming source data at the ETL tier, and (ii) introducing an additional analysis dimension at the OLAP server tier.

Special section on “Spatial data warehouses and SOLAP”

Data Warehouses (DW) and OnLine Analytical Processing (OLAP) tools are used in Business Intelligence (BI) applications in order to support decision-making processes. DWs are a specific type of database used to integrate, accumulate and analyze data from various sources. OLAP tools provide means to query and to analyze the warehoused information and produce online statistical summaries (indicators) at different levels of details. These indicators are computed using aggregate functions (e.g. Sum, Avg, Min, Max, etc.). Users can explore DWs by performing OLAP operations (e.g., Roll-up, Drill-down, etc.). Data mining algorithms can also be used in DWs, aiming towards improving data analysis; these techniques help to discern automatically the correlations and causal links between data. The increasing availability of geo-referenced data has made necessary the need to enrich OLAP with spatial analysis tools. OLAP systems were successfully adapted to solve problems in different areas of application and new kinds of decision support systems, named Spatial Data Warehouses (SDW) and Spatial OLAP (SOLAP) tools have been made available. SDWs are a collection of geographical information supporting spatial analysis. SOLAP tools allow users to perform spatio-temporal exploration of data: these tools combine OLAP analysis with cartographic visualization capabilities of GIS systems. The relevance of SOLAP technology Geoinformatica (2014) 18:269–272 DOI 10.1007/s10707-013-0196-9

Prognostic Risk Estimates of Patients with Multiple Sclerosis and Their Physicians: Comparison to an Online Analytical Risk Counseling Tool

BackgroundPrognostic counseling in multiple sclerosis (MS) is difficult because of the high variability of disease progression. Simultaneously, patients and physicians are increasingly confronted with making treatment decisions at an early stage, which requires taking individual prognoses into account to strike a good balance between benefits and harms of treatments. It is therefore important to understand how patients and physicians estimate prognostic risk, and whether and how these estimates can be improved. An online analytical processing (OLAP) tool based on pooled data from placebo cohorts of clinical trials offers short-term prognostic estimates that can be used for individual risk counseling.ObjectiveThe aim of this study was to clarify if personalized prognostic information as presented by the OLAP tool is considered useful and meaningful by patients. Furthermore, we used the OLAP tool to evaluate patients' and physicians' risk estimates. Within this evaluation process we assessed short-time prognostic risk estimates of patients with MS (final n = 110) and their physicians (n = 6) and compared them with the estimates of OLAP.ResultsPatients rated the OLAP tool as understandable and acceptable, but to be only of moderate interest. It turned out that patients, physicians, and the OLAP tool ranked patients similarly regarding their risk of disease progression. Both patients' and physicians' estimates correlated most strongly with those disease covariates that the OLAP tool's estimates also correlated with most strongly. Exposure to the OLAP tool did not change patients' risk estimates.ConclusionWhile the OLAP tool was rated understandable and acceptable, it was only of modest interest and did not change patients' prognostic estimates. The results suggest, however, that patients had some idea regarding their prognosis and which factors were most important in this regard. Future work with OLAP should assess long-term prognostic estimates and clarify its usefulness for patients and physicians facing treatment decisions.

HANDLING IMPRECISION IN QUALITATIVE DATA WAREHOUSE: URBAN BUILDING SITES ANNOYANCE ANALYSIS USE CASE

Abstract. Data warehouse means a decision support database allowing integration, organization, historisation, and management of data from heterogeneous sources, with the aim of exploiting them for decision-making. Data warehouses are essentially based on multidimensional model. This model organizes data into facts (subjects of analysis) and dimensions (axes of analysis). In classical data warehouses, facts are composed of numerical measures and dimensions which characterize it. Dimensions are organized into hierarchical levels of detail. Based on the navigation and aggregation mechanisms offered by OLAP (On-Line Analytical Processing) tools, facts can be analyzed according to the desired level of detail. In real world applications, facts are not always numerical, and can be of qualitative nature. In addition, sometimes a human expert or learned model such as a decision tree provides a qualitative evaluation of phenomenon based on its different parameters i.e. dimensions. Conventional data warehouses are thus not adapted to qualitative reasoning and have not the ability to deal with qualitative data. In previous work, we have proposed an original approach of qualitative data warehouse modeling, which permits integrating qualitative measures. Based on computing with words methodology, we have extended classical multidimensional data model to allow the aggregation and analysis of qualitative data in OLAP environment. We have implemented this model in a Spatial Decision Support System to help managers of public spaces to reduce annoyances and improve the quality of life of the citizens. In this paper, we will focus our study on the representation and management of imprecision in annoyance analysis process. The main objective of this process consists in determining the least harmful scenario of urban building sites, particularly in dense urban environments.

Decision Support System Based on Data Warehouse

Typical Intelligent Decision Support System is 4-based, its design composes of Data Warehouse, Online Analytical Processing, Data Mining and Decision Supporting based on models, which is called Decision Support System Based on Data Warehouse (DSSBDW). This way takes ETL,OLAP and DM as its implementing means, and integrates traditional model-driving DSS and data-driving DSS into a whole. For this kind of problem, this paper analyzes the DSSBDW architecture and DW model, and discusses the following key issues: ETL designing and Realization; metadata managing technology using XML; SQL implementing, optimizing performance, data mapping in OLAP; lastly, it illustrates the designing principle and method of DW in DSSBDW. Keywords—Decision Support System, Data Warehouse, Data Mining. I. DECISION SUPPORT SYSTEM AND DATA WAREHOUSE A. Decision Support System (DSS) Traditional DSS generally is consisted of three bases model, and IDSS ( Intelligent Decision Support System ) is consisted of four bases structure[1], based on three bases increasing the knowledge base system and its reasoning system, the IDSS is considered of an increase of expert system in the general DSS. Knowledge base is intelligent component in IDSS, used to simulate some smart activity in the human decision making process, that can make DSS supporting to decision makers been greatly enhanced. B. Data Warehouse (DW) B.1 Composition of the DW system DW system contains three levels of architecture, as shown in figure 1. The three levels respectively: data sources, data storage and management , OLAP(On-Line Analytical Processing)and data mining tools. B.2 Data organization structure of DW The data organization style of DW contains three kinds:virtual database relations based on the storage and multidimensional databases. Data Warehouse, the data is divided into four categories: Early details, the details, light degree integrated, highly integrated level. Data Warehouse, there is an important data-metadata (metadata). The data storage environment, there are two main metadata: The first is the operational environment to the data warehouse and conversion of $data, including all source data of members, properties and the data warehouse in the transformation and the two million data for the OLAP and Data Warehouse building map.Figures C. DSS Architecture based on DW DSSBDW[2] (Decision Support System Based on Data Warehouse) compared with traditional DSS, it provided three kinds of decision tools:MDSS(Model DSS), DM, OLAP. The figure 2 can be seen, DSSBDW including DW, Model Analyzing, metadata, OLAP, user interface,etc. II. THE KEY TECHNOLOGY OF DW IN DSS

Extending OCL for OLAP querying on conceptual multidimensional models of data warehouses

The development of data warehouses begins with the definition of multidimensional models at the conceptual level in order to structure data, which will facilitate decision makers with an easier data analysis. Current proposals for conceptual multidimensional modelling focus on the design of static data warehouse structures, but few approaches model the queries which the data warehouse should support by means of OLAP (on-line analytical processing) tools. OLAP queries are, therefore, only defined once the rest of the data warehouse has been implemented, which prevents designers from verifying from the very beginning of the development whether the decision maker will be able to obtain the required information from the data warehouse. This article presents a solution to this drawback consisting of an extension to the object constraint language (OCL), which has been developed to include a set of predefined OLAP operators. These operators can be used to define platform-independent OLAP queries as a part of the specification of the data warehouse conceptual multidimensional model. Furthermore, OLAP tools require the implementation of queries to assure performance optimisations based on pre-aggregation. It is interesting to note that the OLAP queries defined by our approach can be automatically implemented in the rest of the data warehouse, in a coherent and integrated manner. This implementation is supported by a code-generation architecture aligned with model-driven technologies, in particular the MDA (model-driven architecture) proposal. Finally, our proposal has been validated by means of a set of sample data sets from a well-known case study.

Analysis of mealybug incidence on the cotton crop using ADSS-OLAP (Online Analytical Processing) tool

Traditionally the agriculture expert's knowledge is descriptive and experimental, therefore, it is difficult to describe it mathematically and subsequently build agriculture Decision Support Systems (DSS). Furthermore, the corresponding data may be in such a raw form that it cannot be used in a DSS. The Agriculture Decision Support System (ADSS) is a 26-month project based on the Agro-met data from 2001 to 2006 of Punjab (the bread-basket of Pakistan), its ADSS-OLAP, i.e. Online Analytical Processing tool ( www.agroict-olap.org) allows for quick analysis of all possible interesting aggregates of the ADSS data by employing drag-drop and mouse-click and is used in this paper to identify the effective pesticide groups related to the mealybug incidence. Pakistan is the world's fifth-largest producer of cotton, but the emergence of the mealybug as a new cotton pest is likely to reduce the cotton yield by 1.3 million bales. The research work reported in this paper is based on the detailed pest-scouting data of 2300+ farmers of District Multan (cotton hub of Pakistan) for the years 2005 and 2006. This paper will also provide guidelines for the design and development of similar complex systems/tools and discusses the issues of agricultural data-quality management, particularly in the field of insect-pest management.

Graph OLAP: a multi-dimensional framework for graph data analysis

Databases and data warehouse systems have been evolving from handling normalized spreadsheets stored in relational databases, to managing and analyzing diverse application-oriented data with complex interconnecting structures. Responding to this emerging trend, graphs have been growing rapidly and showing their critical importance in many applications, such as the analysis of XML, social networks, Web, biological data, multimedia data and spatiotemporal data. Can we extend useful functions of databases and data warehouse systems to handle graph structured data? In particular, OLAP (On-Line Analytical Processing) has been a popular tool for fast and user-friendly multi-dimensional analysis of data warehouses. Can we OLAP graphs? Unfortunately, to our best knowledge, there are no OLAP tools available that can interactively view and analyze graph data from different perspectives and with multiple granularities. In this paper, we argue that it is critically important to OLAP graph structured data and propose a novel Graph OLAP framework. According to this framework, given a graph dataset with its nodes and edges associated with respective attributes, a multi-dimensional model can be built to enable efficient on-line analytical processing so that any portions of the graphs can be generalized/specialized dynamically, offering multiple, versatile views of the data. The contributions of this work are three-fold. First, starting from basic definitions, i.e., what are dimensions and measures in the Graph OLAP scenario, we develop a conceptual framework for data cubes on graphs. We also look into different semantics of OLAP operations, and classify the framework into two major subcases: informational OLAP and topological OLAP. Second, we show how a graph cube can be materialized by calculating a special kind of measure called aggregated graph and how to implement it efficiently. This includes both full materialization and partial materialization where constraints are enforced to obtain an iceberg cube. As we can see, due to the increased structural complexity of data, aggregated graphs that depend on the underlying “network” properties of the graph dataset are much harder to compute than their traditional OLAP counterparts. Third, to provide more flexible, interesting and informative OLAP of graphs, we further propose a discovery-driven multi-dimensional analysis model to ensure that OLAP is performed in an intelligent manner, guided by expert rules and knowledge discovery processes. We outline such a framework and discuss some challenging research issues for discovery-driven Graph OLAP.

Integration of OLAP and data mining for analysis of results from dependability evaluation experiments

This paper proposes the application of On-Line Analytical Processing (OLAP) and data mining approaches to analyse the large amount of raw data collected in fault injection campaigns and dependability benchmarking experiments. We use data warehousing technologies to store raw results from different experiments in a multidimensional structure where raw data can be analysed by means of OLAP tools. Moreover, we present an approach for identifying the key infrastructural factors determining the behaviour of computer systems in the presence of faults by the application of data mining methods on the data sets. Results obtained with the proposed techniques identified important factors impacting performance and dependability that could not have been revealed solely by the benchmark measures.

Doing more with more information: Changing healthcare planning with OLAP tools

On-line analytical processing (OLAP) is an example of a new breed of tools for decision support that give decision makers the flexibility to customize the selection, aggregation, and presentation of data. To understand the impact of this type of tool, we study an implementation of an OLAP interface on the CATCH data warehouse used by knowledge workers at a regional health planning agency in the State of Florida. The results of a qualitative field study show that after the OLAP implementation, these workers made use of the additional capabilities of OLAP (e.g., aggregation levels and intuitive data manipulation), thereby leveraging their individual abilities to enhance and expand on the tasks they performed for their community. Consequently, they were able to perform in more of a consultative role to their clients, and improved their reputation in the community they serve. This research adds a new dimension to prior research in data warehousing by focusing on the decision support capabilities of OLAP.