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Linked data (LD) has promoted publishing information, and links published information. There are increasing number of LD datasets containing numerical data such as statistics. For this reason, analyzing numerical facts on LD has attracted attentions from diverse domains. This paper aims to support analytical processing for LD data.

This paper proposes a framework called H-SPOOL which provides series of SPARQL (SPARQL Protocol and RDF Query Language) queries extracting objects and attributes from LD data sets, converts them into star/snowflake schemas and materializes relevant triples as fact and dimension tables for online analytical processing (OLAP).

The applicability of H-SPOOL is evaluated using exiting LD data sets on the Web, and H-SPOOL successfully processes the LD data sets to ETL (Extract, Transform, and Load) for OLAP. Besides, experiments show that H-SPOOL reduces the number of downloaded triples comparing with existing approach.

H-SPOOL is the first work for extracting OLAP-related information from SPARQL endpoints, and H-SPOOL drastically reduces the amount of downloaded triples.

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.

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.

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.

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.

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.

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.

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.

This paper attempts to propose a virtual case‐based benchmarking system (VCBS) which incorporates computational intelligence technologies into partners' benchmarking process to support decision‐making.

The proposed system consists of three main modules: data repository module, OLAP module and case‐based reasoning (CBR) module. The VCBS is a web‐based application that enables users to access the system and submit information to the system in anywhere at anytime. The database repository, on the other hand, maintains and acquires the data that are generated in the transactions processes and other workflow processes. It also ensures the entire valuable data which are accessible for the management to make decisions. The OLAP and the CBR modules are considered as the brain of the VCBS. The CBR module is aimed for short‐listing candidate, while the OLAP module is utilized for benchmarking the short‐listed candidate.

The VCBS is particularly useful in situations where multiple supply chain partners are involved to achieve the common objective to produce the products to the best satisfaction of customer demands with the lowest possible cost.

Since data warehouse does not update in real time it only performs update periodically during non‐office hours to avoid network traffic. The solution provided to the company may not be the most updated information.

The proposed system improves the current practice of partner selection by adopting the computational intelligence technologies into the traditional partner selection process with the assimilation of data repository, CBR and OLAP to form the integrated system for evaluation of potential partners prior to the final decision.

To propose an infrastructure of a knowledge‐based system to capture and maintain the procurement information and purchasers' knowledge, regarding how to choose partners in the supply chain network, with the adopting of the neural networks that mimic the operation of human brain to generate solutions systematically.

The proposed system encompasses hybrid artificial intelligence (AI) technologies, Online analytical processing (OLAP) applications and neural networks.

Be able to capture the procurement data and vendors' information that are generated in the workflows to ensure tthat he knowledge and structured information are captured without additional time and effort. Recognizes the void of research in the infrastructure of the hybrid AI technologies for knowledge discovery.

Neural network does not have the sensibility characteristic of the purchasing staff, it is not able to identify the environment changes, which need to re‐adjust the output to fit the environment.

The proposed system obtains useful information related to the trend of sales demand in terms of customer preference and expected requirement using the OLAP module and then based on this information, the neural network provides recommendation related to the supported suppliers that are capable of fulfilling the requirements.

This paper proposes a knowledge‐based system that offers expandability and flexibility to allow users to add more related factors for analysis to enhance the quality of decision making.

Attempts to introduce a quality measurement scheme (QMS) that is able to assess the immediate feedback of customers globally in real time, followed by a data mining process, which is an interactive process that involves assembling the data into a format conducive to producing a multi‐dimensional analysis using an online analytical processing (OLAP) approach. In addition, an XML schema, which provides a universal syntax to facilitate the exchange of data, is used in the design of the QMS to support the data mining process. To validate the feasibility of QMS in real industrial situations, a case example is covered, showing promising test results based on the proposed scheme.

The purpose of this paper is to propose efficient algorithms for structural grouping over Extensible Markup Language (XML) data, called TOPOLOGICAL ROLLUP (T‐ROLLUP), which are to compute aggregation functions based on XML data with multiple hierarchical levels. They play important roles in the online analytical processing of XML data, called XML‐OLAP, with which complex analysis over XML can be performed to discover valuable information from XML.

Several variations of algorithms are proposed for efficient T‐ROLLUP computation. First, two basic algorithms, top‐down algorithm (TDA) and bottom‐up algorithm (BUA), are presented in which the well‐known structural‐join algorithms are used. The paper then proposes more efficient algorithms, called single‐scan by preorder number and single‐scan by postorder number (SSC‐Pre/Post), which are also based on structural joins, but have been modified from the basic algorithms so that multiple levels of grouping are computed with a single scan over node lists. In addition, the paper attempts to adopt the algorithm for parallel execution in multi‐core environments.

Several experiments are conducted with XMark and synthetic XML data to show the effectiveness of the proposed algorithms. The experiments show that proposed algorithms perform much better than the naïve implementation. In particular, the proposed SSC‐Pre and SSC‐Post perform better than TDA and BUA for all cases. Beyond that, the experiment using the parallel single scan algorithm also shows better performance than the ordinary basic algorithm.

This paper focuses on the T‐ROLLUP operation for XML data analysis. For this reason, other operations related to XML‐OLAP, such as CUBE, WINDOWING, and RANKING should also be investigated.

The paper presents an extended version of one of the award winning papers at iiWAS2008.

Data warehousing is the technological trend for the corporate decision support process. This article investigates the current business environment of the data warehouse, including OLAP, data mining, data visualization and other technologies. This article also analyzes the importance of data warehouse management and maintenance and its future developments.

The emergence of advanced information technologies strengthens the capability to the entrepreneur to manage and manipulate data. However, the quality of information, the capability of providing the right information to the right person, and the utilization of information are still in doubt. Therefore, increasing numbers of firms have realized and started to develop as well as improve their existing information systems to fit the ever‐changing business needs of the organization to support decision‐making for the volatile business environment. Indeed, previous research studies have found that logistics management is the great frontier of cost reduction. Therefore, in this paper, an infrastructure of a decision support system is proposed to capture and maintain the business and resources allocation information with the adoption of the neural network for its artificial intelligent characteristic that mimic the operation of human brain to generate solutions systematically. The proposed system is adopted by a shipping company to assist allocation of containers.

Managing enterprise performance is an important, yet a difficult process due to its complexity. The process involves monitoring the strategic focus of an enterprise, whose performance is measured from the analysis of data generated from a wide range of interrelated business activities performed at different levels within the enterprise. This study aims to investigate management data systems technologies in terms of how they are used and the issues that are related to their effective management within the broader context of enterprise performance management (EPM).

A range of recently published research literature on data warehousing, online analytic processing and EPM is reviewed to explore their current state, issues and challenges learned from their practice.

The findings of the study are reported in two parts. The first part discusses the current business practices of these technologies, and the second part identifies and discusses the issues and challenges the business managers dealing with these technologies face for gaining competitive advantage for their businesses.

The study findings are intended to assist the business managers to effectively understand the issues and technologies behind EPM implementation.