Models of multi-dimensional analysis for qualitative data and its application

Building Data Warehouse for Diseases Registry: First Step for Clinical Data Warehouse

In the context of scientometrics and bibliometrics, several research fields are dealing with bibliographic data. In this paper, we will explore how the combination of online analytical processing (OLAP) analysis and information networks could be an interesting issue. In Business Intelligence, OLAP is a technology supported by data warehousing systems. It provides tools for analyzing data according to multiple dimensions and multiple hierarchical levels. At the same time, several information networks (co-authors network, citations network, institutions network, etc.) can be built based on bibliographic databases. Originally, OLAP was introduced to analyze structured data. However, in this paper, we wonder if, by combining OLAP and information networks, we can provide a new way of analyzing bibliographic data. OLAP should be able to handle information networks and be also useful for monitoring, browsing and analyzing the content and the structure of bibliographic networks. The goal of this survey paper is to review previous work on OLAP and information networks dealing with bibliographic data. We also propose a comparison between traditional OLAP and OLAP on information networks and discuss the challenges OLAP faces regarding bibliographic networks.

Chapter 64 - Exploiting Versions for On-line Data Warehouse Maintenance in MOLAP Servers

In today's knowledge-, service-, and cloud-based economy, businesses accumulate massive amounts of data from a variety of sources. In order to understand businesses one may need to perform considerable analytics over large hybrid collections of heterogeneous and partially unstructured data that is captured related to the process execution. This data, usually modeled as graphs, increasingly come to show all the typical properties of big data: wide physical distribution, diversity of formats, non-standard data models, independently-managed and heterogeneous semantics. We use the term big process graph to refer to such large hybrid collections of heterogeneous and partially unstructured process related execution data. Online analytical processing (OLAP) of big process graph is challenging as the extension of existing OLAP techniques to analysis of graphs is not straightforward. Moreover, process data analysis methods should be capable of processing and querying large amount of data effectively and efficiently, and therefore have to be able to scale well with the infrastructure's scale. While traditional analytics solutions (relational DBs, data warehouses and OLAP), do a great job in collecting data and providing answers on known questions, key business insights remain hidden in the interactions among objects: it will be hard to discover concept hierarchies for entities based on both data objects and their interactions in process graphs. In this paper, we introduce a framework and a set of methods to support scalable graph-based OLAP analytics over process execution data. The goal is to facilitate the analytics over big process graph through summarizing the process graph and providing multiple views at different granularity. To achieve this goal, we present a model for process OLAP (P-OLAP) and define OLAP specific abstractions in process context such as process cubes, dimensions, and cells. We present a MapReduce-based graph processing engine, to support big data analytics over process graphs. We have implemented the P-OLAP framework and integrated it into our existing process data analytics platform, ProcessAtlas, which introduces a scalable architecture for querying, exploration and analysis of large process data. We report on experiments performed on both synthetic and real-world datasets that show the viability and efficiency of the approach.

A company, majorly company that active in commercial (profit orientation) need to analyze their sales performance. By analyzing sales performance, company can increase their sales performance. One of method to analyze sales performance is by collecting historical data that relates to sales and then process that data so that produce information that show company sales performance. A data warehouse is a set of data that has characteristic subject oriented, time variant, integrated, and nonvolatile that help company management in processing of decision making. Design of data warehouse is started from collecting data that relate to sales such as product, customer, sales area, sales transaction, etc. After collecting the data, next is data extraction and transformation. Data extraction is a process f or selecting data that will be loaded into data warehouse. Data transformation is making some change to the data afte r extracted to be more consistent. After transformation processing, data are loaded into data warehouse. Data in data warehouse is processed by OLAP (On Line Analytical Processing) to produce information. Information that are produced from data processing by OLAP are chart and query reporting. Chart reporting are sales chart based on cement type, sales chart based on sales area, sales chart based on plant, monthly and year ly sales chart, and chart based on customer feedback. Query reporting are sales based on cement type, sales area, plant and customer. Keywords: Data warehouse; OLAP; Sales performance analysis; Ready mix market

SummaryThe Model‐Driven Architecture (MDA) is an approach that aligns modeling and automation for software development. By applying such an approach to data warehouse (DW) projects, we can minimize a great deal of time and cost. Furthermore, most of OnLine Analytical Processing (OLAP) platforms seem to be like black boxes that provide wizards only to business intelligence developers to create and manipulate OLAP objects without allowing their sustainability and migration from a platform to another. That is why many works in the literature have proposed using the MDA approach in DW projects. However, most of them have mainly focused on the generation of the DW relational model from the conceptual one, and they overlooked the OLAP model and the cube implementation.To deal with this problem, we propose in this paper an MDA solution to automate the process of getting OLAP cube and its implementation through a set of metamodels and automatic transformations among them. In fact, the proposal generates the OLAP and DW relational models (PSMs) from the conceptual one, using also a PDM model that describes the target business intelligence platform. After that, the source code to create the cube is got from both PSM models. For this aim, we define a set of transformation rules implemented using the Atlas transformation language. Finally, a case study will be provided to validate our approach.

Many modern applications, including online financial feeds, tag-based mass transit systems and RFID-based supply chain management systems transmit real-time data streams. There is a need for a special-purpose event stream processing technology to analyze this vast amount of sequential multi-dimensional data to enable online, operational decision making. Existing techniques such as traditional online analytical processing (OLAP) systems are not designed for real-time pattern-based operations, while state-of-the-art Complex Event Processing (CEP) systems designed for sequence detection do not support OLAP operations. Supporting complex pattern queries at different concept and pattern hierarchies must be devised by providing efficient computation and data sharing. In this dissertation, we propose a novel E-Cube model that combines CEP and OLAP techniques for multi-dimensional event pattern analysis at different abstraction levels. Further, we go beyond the linear sequence pattern queries targeted by ECube core system towards supporting an expressive composite pattern query language (composed of arbitrarily nested sequence, negation, recursion, AND and OR operators) to express powerful pattern matching requests.

Online Analytical Processing (OLAP) comprises tools and algorithms that allow querying multidimensional databases. It is based on the multidimensional model, where data can be seen as a cube such that each cell contains one or more measures that can be aggregated along dimensions. In a Big Data scenario, traditional data warehousing and OLAP operations are clearly not sufficient to address current data analysis requirements, for example, social network analysis. Furthermore, OLAP operations and models can expand the possibilities of graph analysis beyond the traditional graph-based computation. Nevertheless, there is not much work on the problem of taking OLAP analysis to the graph data model. This paper proposes a formal multidimensional model for graph analysis, that considers the basic graph data, and also background information in the form of dimension hierarchies. The graphs in this model are node- and edge-labelled directed multi-hypergraphs, called graphoids, which can be defined at several different levels of granularity using the dimensions associated with them. Operations analogous to the ones used in typical OLAP over cubes are defined over graphoids. The paper presents a formal definition of the graphoid model for OLAP, proves that the typical OLAP operations on cubes can be expressed over the graphoid model, and shows that the classic data cube model is a particular case of the graphoid data model. Finally, a case study supports the claim that, for many kinds of OLAP-like analysis on graphs, the graphoid model works better than the typical relational OLAP alternative, and for the classic OLAP queries, it remains competitive.

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).

Computing multiple related group-bys and aggregates is one of the core operations of On-Line Analytical Processing (OLAP) applications. Recently, Gray et al. [GBLP95] proposed the “Cube” operator, which computes group-by aggregations over all possible subsets of the specified dimensions. The rapid acceptance of the importance of this operator has led to a variant of the Cube being proposed for the SQL standard. Several efficient algorithms for Relational OLAP (ROLAP) have been developed to compute the Cube. However, to our knowledge there is nothing in the literature on how to compute the Cube for Multidimensional OLAP (MOLAP) systems, which store their data in sparse arrays rather than in tables. In this paper, we present a MOLAP algorithm to compute the Cube, and compare it to a leading ROLAP algorithm. The comparison between the two is interesting, since although they are computing the same function, one is value-based (the ROLAP algorithm) whereas the other is position-based (the MOLAP algorithm). Our tests show that, given appropriate compression techniques, the MOLAP algorithm is significantly faster than the ROLAP algorithm. In fact, the difference is so pronounced that this MOLAP algorithm may be useful for ROLAP systems as well as MOLAP systems, since in many cases, instead of cubing a table directly, it is faster to first convert the table to an array, cube the array, then convert the result back to a table.

Data cube has been playing an essential role in fast OLAP (online analytical processing) in many data warehouses. The pre-computation of data cubes is critical for improving the OLAP response time of in large high-dimensional data warehouses. However, as the sizes of data warehouses grow, the time it takes to perform this pre-computation becomes a significant performance bottleneck. In a high dimensional data warehouse, it might not be practical to build all these cuboids and their indices. In this paper, we propose a hierarchical cubing algorithm to partition the high dimensional data cube into low dimensional cube segments. It permits a significant reduction of CPU and I/O overhead for many queries by restricting the number of cube segments to be processed for both the fact table and bitmap indices. Experimental results show that the proposed method is significantly more efficient than other existing cubing methods.

Business data collection is growing exponentially in recent years. A variety of industries and businesses have adopted new technologies of data storages such as data warehouses. On Line Analytical Processing (OLAP) has become an important tool for executives, managers, and analysts to explore, analyze, and extract interesting patterns from enormous amount of data stored in data warehouses and multidimensional databases. However, it is difficult for human analysts to interpret and extract meaningful information from large amount of data if the data is presented in textual form as relational tables. Visualization and interactive tools employ graphical display formats that help analysts to understand and extract useful information fast from huge data sets. This paper presents a new visual interactive exploration technique for an analysis of multidimensional databases. Users can gain both overviews and refine views on any particular region of interest of data cubes through the combination of interactive tools and navigational functions such as drilling down, rolling up, and slicing. Our technique allows users who are not experts in OLAP technology to explore and analyze OLAP data cubes and data warehouses without generating sophisticated queries. Furthermore, the visualization in our technique displays the exploration path enhancing the user's understanding of the exploration.

Radio Frequency Identification (RFID) technology is used in many applications for monitoring object movement. The use of RFID in supply chain management systems enables to track the movement of products from suppliers to warehouses, store backrooms, and eventually points of sale. The vast amount of data resulting from the proliferation of RFID readers and tags poses challenges for data management and analytics. RFID data warehousing can enhance data quality and consistency, and give great potential benefits for Online Analytical Processing (OLAP) applications. Traditional data warehouses are built primarily on relational database management systems. However, the size of RFID data being collected and analyzed in the industry for business intelligence is growing rapidly, making traditional warehousing solutions prohibitively expensive. Hive is an open-source data warehousing solution built on top of Hadoop which is a popular Big Data computing framework. This paper presents alternative RFID data warehouse designs which can handle a large amount of RFID data and support a variety of OLAP queries. The proposed approaches are implemented on Hive and evaluated for query performance in cloud computing environment.

Traditional data warehousing technologies and On-Line Analytical Processing (OLAP) are unable to analyze textual data. Moreover, as OLAP queries of a decision-maker are generally related to a context, contextual information must be taken into account during the exploitation of data warehouses. Thus, we propose a contextual text cube model denoted CXT-Cube which considers several contextual factors during the OLAP analysis in order to better consider the contextual information associated with textual data. CXT-Cube is characterized by several contextual dimensions, each one related to a contextual factor. In addition, we extend our aggregation OLAP operator for textual data ORank (OLAP-Rank) to consider all the contextual factors defined in our CXT-Cube model. To validate our model, we perform an experimental study and the preliminary results show the importance of our approach for integrating textual data into a data warehouse and improving the decision-making.

Finding an application-appropriate model for XML data warehouses