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Association rules are used in different data mining applications, including Web mining, intrusion detection, and bioinformatics. This study mainly discusses the COVID‐19 patient diagnosis and treatment data mining algorithm based on association rules. General data The key time interval during the main diagnosis and treatment process (including onset to dyspnea, first diagnosis, admission, mechanical ventilation, death, and the time from first diagnosis to admission, etc.), the cause of death by laboratory examination, and so forth. The frequency of drug use was counted and association rule algorithm was used to analyse and study the effect of drug treatment. The results could provide reference for rational drug use in COVID‐19 patients. In this study, in order to improve the efficiency of data mining in data processing, it is necessary to pre‐process these data. Secondly, in the application of this data mining, the main objective is to extract association rules of COVID‐19 complications. So its properties for mining should be various diseases. Therefore, it is necessary to classify individual disease types. During the construction of association rules database, the data in the data warehouse is analysed online and the association rules data mining is analysed. The results are stored in the knowledge base for decision support. For example, the prediction results of the decision tree can be displayed at this level. After the construction of the mining model, the display interface can be mined, and the decision‐maker can input the corresponding attribute value and then predict it. 0.76% of people had both COVID‐19, CHD and hypertension, while 46.5% of people with COVID‐19 and CHD were likely to have hypertension. This study is helpful to analyse the imaging factors of COVID‐19 disease. [ABSTRACT FROM AUTHOR]

Statistical reasoning was one of the earliest methods to draw insights from data. However, over the last three decades, association rule mining and online analytical processing have gained massive ground in practice and theory. Logically, both association rule mining and online analytical processing have some common objectives, but they have been introduced with their own set of mathematical formalizations and have developed their specific terminologies. Therefore, it is difficult to reuse results from one domain in another. Furthermore, it is not easy to unlock the potential of statistical results in their application scenarios. The target of this paper is to bridge the artificial gaps between association rule mining, online analytical processing and statistical reasoning. We first provide an elaboration of the semantic correspondences between their foundations, i.e., itemset apparatus, relational algebra and probability theory. Subsequently, we propose a novel framework for the unification of association rule mining, online analytical processing and statistical reasoning. Additionally, an instance of the proposed framework is developed by implementing a sample decision support tool. The tool is compared with a state-of-the-art decision support tool and evaluated by a series of experiments using two real data sets and one synthetic data set. The results of the tool validate the framework for the unified usage of association rule mining, online analytical processing, and statistical reasoning. The tool clarifies in how far the operations of association rule mining and online analytical processing can complement each other in understanding data, data visualization and decision making.

In a number of situations public sector information analysis uses to be performed empirically without any concrete procedure which should be based on the use of some models or even the use of collected data analysis tools. At the same time, the issue of data turning into conclusive information for a public institution does not benefit from an efficient development framework. On the contrary, the idea of systemized data analysis lacks completely in the case of most public institutions. This paper is intended as a case study of data analysis by means of an Online Analytical Processing (OLAP) system with real data from the Area Labour Inspectorate of Alba County (Romania) to prove both the efficiency and the need to create such a system. The aim is to prove the beneficial role of OLAP technology implementation in public institutions, at any level which may involve a managerial act.

Social platforms, such as Twitter, reveal much about the tastes of the public. Many studies focus on the content analysis of social platforms, which assists in product promotion and sentiment investigation. On the other hand, online analytical processing (OLAP) has been proven to be very effective for analyzing multidimensional structured data. The key purpose of applying OLAP to text messages, (e.g., tweets), called text OLAP, is to mine and construct the hierarchical dimension based on the unstructured text content. In contrast to the plain texts which text OLAP usually handles, the social media content includes a wealth of social relationship information which can be employed to extract a more effective dimensional hierarchy. In this paper, we propose a topic model called twitter hierarchical latent Dirichlet allocation (thLDA). Based on hierarchical latent Dirichlet allocation, thLDA aims to automatically mine the hierarchical dimension of tweets' topics, which can be further employed for text OLAP on the tweets. Furthermore, thLDA uses word2vec to analyze the semantic relationships of words in tweets to obtain a more effective dimension. We conduct extensive experiments on huge quantities of Twitter data and evaluate the effectiveness of thLDA. The experimental results demonstrate that it outperforms other current topic models in mining and constructing the hierarchical dimension of tweeters' topics.

A database management system is a constant application of science that provides a platfonn for the creation, movement, and use of voluminous data. The area has witnessed a series of developments and technological advancements from its conventional structured database to the recent buzzword, bigdata. This paper aims to provide a complete model of a relational database that is still being widely used because of its well known ACID properties namely, atomicity, consistency, integrity and durability. Specifically, the objective of this paper is to highlight the adoption of relational model approaches by bigdata techniques. Towards addressing the reason for this in corporation, this paper qualitatively studied the advancements done over a while on the relational data model. First, the variations in the data storage layout are illustrated based on the needs of the application. Second, quick data retrieval teclmiques like indexing, que1y processing and concu!l'ency control methods are revealed. The paper provides vital insights to appraise the efficiency of the structured database in the unstructure.d enviromnent, pa1ticularly when both consistency and scalability become an issue in the working of the hybrid transactional and analytical database management system. [ABSTRACT FROM AUTHOR]

Graph OLAP is a technology that generates aggregates or summaries of a large-scale graph based on the properties (or dimensions) associated with its nodes and edges, and in turn enables interactive analyses of the statistical information contained in the graph. To efficiently support these OLAP functions, a graph cube is widely used, which maintains aggregate graphs for all dimensions of the source graph. However, computing the graph cube for a large graph requires an enormous amount of time. While previous approaches have used the MapReduce framework to cut down on this computation time, the recently developed Spark environment offers superior computational performance. To leverage the advantages of Spark, we propose the GraphNaïve and GraphTDC algorithms. GraphNaïve sequentially computes graph cuboids for all dimensions in a graph, while GraphTDC computes them after first creating an execution plan. We also propose the Generate Multi-Dimension Table method to efficiently create a multidimensional graph table to express the graph. Evaluation experiments demonstrated that the GraphTDC algorithm significantly outperformed Spark SQL's built-in library DataFrame, as the size of graphs increased. [ABSTRACT FROM AUTHOR]

Materialized views are used as an alternative means for reducing the response time of analytical queries posed against a data warehouse. Since all views cannot be materialized and since optimal view selection is an NP-Hard problem, there is a need to select an appropriate subset of views for materialization that reduce the response times for analytical queries. This problem, referred to as view selection, is a widely studied problem in data warehousing. Several materialized view selection (MVS) algorithms exist that address the view selection problem, as a single objective optimization problem where the objective is to minimize the total cost of evaluating all the views (TVEC). This cost comprises two costs, i.e. the total cost of evaluation due to materialized views and the total cost of evaluation due to non-materialized views. Minimization of these two costs simultaneously would lead to the minimization of TVEC. In this paper, this bi-objective optimization problem, where the two costs are minimized simultaneously, has been solved using the Multi-Objective Genetic Algorithm (MOGA). The proposed MOGA based MVS algorithm selects the Top-K views from a multidimensional lattice with the purpose of achieving an optimal trade-off between the two aforementioned objectives. Materializing these selected Top-K views would reduce the response times for analytical queries and thereby would result in effective and efficient decision making. [ABSTRACT FROM AUTHOR]

In this paper, a co-operative distributed process mining system (CDPMS) is developed to streamline the workflow along the supply chain in order to offer shorter delivery times, more flexibility and higher customer satisfaction with learning ability. The proposed system is equipped with the 'distributed process mining' feature which is used to discover the hidden relationships among each working decision in distributed manner. This method incorporates the concept of data mining and knowledge refinement into decision making process for ensuring 'doing the right things' within the workflow. An example of implementation is given, based on the case of slider manufacturer. [ABSTRACT FROM AUTHOR]

The BI (Business Intelligence) has gained great success during the last decade throughout the world to aid in decision support with the availability of necessary knowledge to reduce costs, increase revenues and minimize risks. The local market of Pakistan is still not very much aware of its benefits, except some multinationals which are using these tools since almost 7-8 years and earning more revenues and improved performances and a few others are under the process of implementation. The small and medium sized businesses of our local market are focused for the implementation of BI. The pros and cons are identified by analysing the BI tools being used by other large companies here in Pakistan and feasibility of these tools at small and medium enterprises is discussed so that they too may focus on their KPIs (Key Performance Indicator) to increase their performance level.

Pilot briefings, in their traditional form, drown pilots in a sea of information. Rather than unfocused swathes of air traffic management (ATM) information, pilots require only the information for their specific flight, preferably with an emphasis on the most important information. In this paper, we introduce the notion of ATM information cubes – in analogy to the well-established concept of Online analytical processing (OLAP) cubes in data warehousing. We propose a framework with merge and abstraction operations for the combination and summarization of the information in ATM information cubes to obtain management summaries of relevant information. To this end, we adopt the concept of semantic data container – a package of data items with a semantic description of the contents. The semantic descriptions then serve to hierarchically organise semantic containers along the dimensions of an ATM information cube. Leveraging this hierarchical organisation, a merge operation combines ATM information from individual semantic containers and collects the data items into composite containers. An abstraction operation summarises the data items within a semantic container, replacing individual data items with more abstract data items with summary information. [ABSTRACT FROM AUTHOR]

Recently, there are some online quantile algorithms that work on how to analyze the order statistics about the high-volume and high-velocity data stream, but the drawback of these algorithms is not scalable because they take the GK algorithm as the subroutine, which is not known to be mergeable. Another drawback is that they can't maintain the correctness, which means the error will increase during the process of the window sliding. In this paper, we use a novel data structure to store the sketch that maintains the order statistics over sliding windows. Therefore three algorithms have been proposed based on the data structure. And the fixed-size window algorithm can keep the sketch of the last W elements. It is also scalable because of the mergeable property. The time-based window algorithm can always keep the sketch of the data in the last T time units. Finally, we provide the window aggregation algorithm which can help extend our algorithm into the distributed system. This provides a speed performance boost and makes it more suitable for modern applications such as system/network monitoring and anomaly detection. The experimental results show that our algorithm can not only achieve acceptable performance but also can actually maintain the correctness and be mergeable. [ABSTRACT FROM AUTHOR]

Sustainable competitive advantage for universities could not be separated from the information technology. Actually, some universities have been implemented this information technology, especially virtual office applications. Eventhough, the virtual office applications were implemented, but preliminary research found that those applications used were Online Transaction Processing only, not Online Analytical Processing that can be used to make decision. Based on those explanations, this research had been done to explore and design the virtual office prototype application for universities. Three methods were used to conduct this reseach, which were explorative, validation, and application design. Finally, prototype applications of virtual office dashboard, lecturer dashboard and accreditation dashboard to improve sustainable competitive advantage for universities were presented as this study result.

The paper analyses the business data analysis technologies, provides their classification and considers relevant terminology. The feasibility of business data analysis technologies handling big data sources is overviewed. The paper shows the results of examination of the online big data source analytics technologies, data mining and predictive modelling technologies and their trends.

OLAP (online analytical processing) is a key technology of business intelligence based on relational data. In big data era, people want to achieve high performance OLAP using a large cluster of ordinary nodes. However, the performance of such systems is a big challenge. Recently, many SQL on Hadoop systems have been proposed to address this challenge. We have seen a significant performance improvement of such systems. A survey of technology development of OLAP technologies was first provided. Then, a study of the performance of three representatives SQL on Hadoop systems was focused on. Based on the results, it is expected that such systems will play an very important role in the market of low cost OLAP analysis.

In view of problems of low accuracy and inadequate decision support existed in analysis and control of production cost of coal enterprises, the paper proposed a design scheme of production cost management and decision support. The scheme uses data market to collect the production cost in whole production range and make real-time collection and assignment for the production cost, and it uses technologies of online analytical processing and data mining to get law and trend hided in internal of the production cost, so as to provide decision support for decision-maker. The actual application shows that the scheme realizes real-time analysis and control for the production cost.

Aiming at the unstructured brain data and data-driven research process, provenances have become an important component of brain and health big data rather than the accessory of raw experimental data in the systematic Brain Informatics (BI) study. However, the existing file-based or transaction-database-based provenance queries cannot effectively support quickly understanding data and generating decisions or suppositions in the systematic BI study, which need multi-aspect and multi-granularity provenance information and a process of incremental modification. Inspired by studies on the data warehouse and online analytical processing (OLAP) technology, this paper proposes a BI provenance warehouse. The provenance cube and basic OLAP operations are defined. A complete Data-Brain-based development approach is also designed. Such a BI provenance warehouse represents a radically new way for developing the brain big data center, which regards raw experimental data, provenances and domain ontologies as different levels of brain big data for data sharing and mining. [ABSTRACT FROM AUTHOR]

The BI (Business Intelligence) has gained great success during the last decade throughout the world to aid in decision support with the availability of necessary knowledge to reduce costs, increase revenues and minimize risks. The local market of Pakistan is still not very much aware of its benefits, except some multinationals which are using these tools since almost 7-8 years and earning more revenues and improved performances and a few others are under the process of implementation. The small and medium sized businesses of our local market are focused for the implementation of BI. The pros and cons are identified by analysing the BI tools being used by other large companies here in Pakistan and feasibility of these tools at small and medium enterprises is discussed so that they too may focus on their KPIs (Key Performance Indicator) to increase their performance level. [ABSTRACT FROM AUTHOR]

The paper considers automation problems of the interface formation between a table and a relational database. The task description is formalized and the description of the existing approaches to formation of data representations on an example of widely widespread CASE-tools is submitted. The definition of intermediate data representation as a ”join table” is offered, which is used for maintenance of correctness of data representation formation, and also is necessary for direct and inverse data transformations. On the basis of lossless join property and realized dependencies, the concept and a way of context formation of the application and restrictions is introduced. The considered material is further used for constructing an inverse data transformation from tabular presentation into a relational one. On the basis of relationships properties on a database scheme, the partial order on the relations is established, and the restriction of acyclic databases schemes is introduced. The received results are further used at the analysis of principles of formation of inverse data transformation, and the basic details of such a transformation algorithm are considered.

This article introduces Linked Open Cube Analytics (LOCA) systems for performing statistical analysis on top of combined data. It explores LOCA systems' potential and challenges and presents a proof of concept of such a system. [ABSTRACT FROM PUBLISHER]

Reference models for data analysis with data warehouses may consist of multidimensional reference models and analysis graphs. Multidimensional reference models are best-practice domain-specific data models for online analytical processing. Analysis graphs are reference models of analysis processes for event-driven data analysis. Small and medium-sized enterprises (SMEs) as well as large multinational companies may benefit from the use of reference models for data analysis. The availability of multidimensional reference models lowers the obstacles that inhibit SMEs from using business intelligence (BI) technology. Multinational companies may define multidimensional reference models for increased compliance among subsidiaries and departments. Furthermore, the definition of analysis graphs facilitates the handling of business events for both SMEs and large companies. Modelers may customize the chosen reference models, tailoring the models to the specific needs of the individual company or local subsidiary. Customizations may consist of additions, omissions, and modifications with respect to the reference model. In this paper, we propose a metamodel and customization approach for multidimensional reference models and analysis graphs. We specifically address the explicit modeling of key performance indicators as well as the definition of analysis situations and analysis graphs. [ABSTRACT FROM AUTHOR]

In self-hosted environments it was feared that business intelligence (BI) will eventually face a resource crunch situation due to the never ending expansion of data warehouses and the online analytical processing (OLAP) demands on the underlying networking. Cloud computing has instigated a new hope for future prospects of BI. However, how will BI be implemented on Cloud and how will the traffic and demand profile look like? This research attempts to answer these key questions in regards to taking BI to the Cloud. The Cloud hosting of BI has been demonstrated with the help of a simulation on OPNET comprising a Cloud model with multiple OLAP application servers applying parallel query loads on an array of servers hosting relational databases. The simulation results reflected that extensible parallel processing of database servers on the Cloud can efficiently process OLAP application demands on Cloud computing. [ABSTRACT FROM AUTHOR]

Copyright of Romanian Journal of Human - Computer Interaction is the property of Matrix Rom and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract: This paper proposes a web-based construction waste estimation system (WCWES) for building construction projects incorporating the concepts of work breakdown structure, material quantity takeoff, material classification, material conversion ratios, material wastage levels, and the mass balance principle. The WCWES integrates online data input modules and online analytical modules for the quantification of different kinds of waste generated in the construction process at the project level. It facilitates accessibility, interfacing, connectivity and information sharing of users in carrying out a wide range of construction waste estimation tasks for sustainable construction waste management. A hypothetical building construction project is used to demonstrate the application and usefulness of the WCWES. [Copyright &y& Elsevier]

The usage of online analytical processing is now widespread, having emerged primarily in areas of business information systems using relational databases. However, the XML tree-based model is quite different from the relational model in mainstream OLAP. In terms of conceptual modelling, this provides new challenges, for example with operations such as cube and roll-up. However, our view is that it should be possible to exploit the more expressive XML model to deliver efficiencies not possible in more traditional OLAP systems. To be precise, XML data contains inherent structure and semantics not available in relational data. In this paper, we analyse the distinct characteristics and requirements of a more structured OLAP to make comprehensive comparisons between structural and flat dimensions. In order to build our conceptual model, we examined different XML cube construction models for commonly used XML recursive structures. This construction process requires only a single scan of input data and captures structural information on the fly, delivering both standard and structural OLAP support simultaneously. [ABSTRACT FROM AUTHOR]

This study discusses an online reporting system used for waste flow tracking (auditing) and investigation of violations. In contrast to systems used in other countries, this system requires waste generators and transporters, as well as the treatment, storage, and disposal facilities (TSDFs) to file online reports on the movement of industrial waste within 24 h of being shipped, received, or completely treated. This system also provides online analytical processing for reporters to analyze all their data, thereby encouraging self-management of waste. Currently there are 25,861 generators, 4,963 transporters, and 865 TSDFs in Taiwan that are required to file online reports. Approximately 80 % of all waste generated in Taiwan is reported using this system. More than 7,000 vehicles are equipped with a global positioning system (GPS) for waste tracking, among which over 2,000 transport hazardous waste. Disposal vehicles transmit their locations to the Control Center every 30 s. Database quality has been enhanced through auditing and inspection for more than 12 years since this system was established. It serves as a 'single portal' for industrial waste management and facilitates informed decision making for resource exchange and material flow among various industries to achieve resource and environmental sustainability. [ABSTRACT FROM AUTHOR]

In this paper, the system bgBell/OLAP for warehousing and online analytical processing data about unique Bulgarian bells is proposed. The implemented system provides possibility for retrieving summarized reports and analyzing different characteristics of the bells with the purpose of extracting the previously unknown and potentially useful information. [ABSTRACT FROM AUTHOR]

Almost all industries need to perform effective data analysis of their business process data. However, methodologies for such analysis will differ depending on the type of data to be analyzed. A steel manufacturing process consists of sequence of processing units having many complex sub processes. In each processing unit of steel manufacturing, various sensors are used to sense and collect data for the control of the processes as well as to maintain and improve the quality of the steel product. Processing as well as analysis of this data carries lot of significance. As conventional methods of analysis of these data are neither suitable nor efficient due to huge and diverse data to be analyzed, we must apply methods that make best use of modern technologies to obtain desired results. This paper presents design and implementation of an effective analysis system 'OA-SPQC' (Online Analysis- Statistical Process and Quality Control) System that makes use of statistical process control, quality control and Online analytical processing (OLAP) with the goal of analyzing process and quality control parameters effectively and further to provide flexibility to the user for necessary analysis in a typical mild steel manufacturing process. [ABSTRACT FROM AUTHOR]

The computation of data cubes is one of the most expensive operations in on-line analytical processing (OLAP). To improve efficiency, an iceberg cube represents only the cells whose aggregate values are above a given threshold (minimum support). Top-down and bottom-up approaches are used to compute the iceberg cube for a data set, but both have performance limitations. In this paper, a new algorithm, called Multi-Tree Cubing (MTC), is proposed for computing an iceberg cube. The Multi-Tree Cubing algorithm is an integrated top-down and bottom-up approach. Overall control is handled in a top-down manner, so MTC features shared computation. By processing the orderings in the opposite order from the Top-Down Computation algorithm, the MTC algorithm is able to prune attributes. The Bottom Up Computation (BUC) algorithm and its variations also perform pruning by relying on the processing of intermediate partitions. The MTC algorithm, however, prunes without processing such partitions. The MTC algorithm is based on a specialized type of prefix tree data structure, called an Attribute–Partition tree (AP-tree), consisting of attribute and partition nodes. The AP-tree facilitates fast, in-memory sorting and APRIORI-like pruning. We report on five series of experiments, which confirm that MTC is consistently as fast or faster than BUC, while finding the same iceberg cubes. [ABSTRACT FROM AUTHOR]

Logistic regression is an important technique for analyzing and predicting date with categorical attributes. In this paper, we consider supporting online analytical processing (OLAP) of logistic regression analysis for multidimensional data in a data cube where it is expensive in time and space to build logistic regression models for each cell from the raw data. We propose a novel scheme to compress the data in such a way that we can reconstruct logistic regression models to answer any OLAP query without accessing the raw data. Based on a first-order approximation to the maximum likelihood estimating equations, we develop a compression scheme that compresses each base cell into a small compressed data block with essential information to support the aggregation of logistic regression models. Aggregation formulas for deriving high-level logistic regression models from lower level component cells are given. We prove that the compression is asymptotically lossless in the sense that the aggregated estimator deviates from the true model by an error that is bounded and approaches to zero when the data size increases. The results show that the proposed compression and aggregation scheme can make feasible OLAP of logistic regression in a data cube. Further, it supports real-time logistic regression analysis of stream data, which can only be scanned once and cannot be permanently rotained. Experimental results validate our theoretical analysis and demonstrate that our method can dramatically save time and space costs with almost no degradation of the modeling accuracy. [ABSTRACT FROM AUTHOR]

Users of a business intelligence (BI) system employ an approach referred to as online analytical processing (OLAP) to view multidimensional data from different perspectives. Query languages, e.g., SQL or MDX, allow for flexible querying of multidimensional data but query formulation is often time-consuming and cognitively challenging for many users. Alternatives to using a query language, e.g., graphical OLAP clients, parameterized reports, or dashboards, are often not a full-blown alternative to using a query language. Experience in cooperative research projects with industry led to the following observations regarding the use of OLAP queries in practice. First, within the same organization, similar OLAP queries are repeatedly composed from scratch in order to satisfy similar information needs. Second, across different organizations and even domains, OLAP queries with similar structures are repeatedly composed from scratch. Finally, vague requirements regarding frequently composed OLAP queries in the early stages of a project potentially lead to rushed development in later stages, which can be alleviated by following best practices for OLAP query composition. In engineering, knowledge about best-practice solutions to frequently arising challenges is often documented and represented using patterns. In that spirit, an OLAP pattern describes a generic solution for composing a query that allows a BI user to satisfy a certain type of information need given fragments of a conceptual model. This paper introduces a formal definition of OLAP patterns as well as an expressive, flexible, and generally applicable definition language. • Individual OLAP queries are insufficient for documenting and sharing best-practice solutions for analytical problems. • OLAP patterns document best-practice solutions for satisfying generic information needs • Executable queries can be automatically obtained through instantiation of OLAP patterns. • OLAP patterns can be shared within organizations as well as across organizations and even across domains. • Applicability of OLAP patterns in a specific context is checked via parameters and constraints. [ABSTRACT FROM AUTHOR]

In today’s globally competitive industries, high-quality and high-reliability products play an important role in achieving customer satisfaction, and insisting on quality is always the only way to survive in an enterprise. Studies indicate that automating quality audits and adding decision support in quality improvement is an attractive idea. In this environment, production workflow mining is an approach for extracting knowledge from different manufacturing processes in order to assist real-time quality prediction and improvement. This papers attempts to propose an intelligent production workflow mining system (IPWMS) embracing online analytical processing (OLAP) and data mining technology, together with the use of artificial intelligence combining artificial neural networks (ANNs) and fuzzy rule sets to realize knowledge discovery and decision support in high-quality manufacturing. To validate the feasibility of the proposed system, a prototype is developed and evaluated in a company, and a description of this case example is covered in this paper. [ABSTRACT FROM AUTHOR]

This study surveyed data warehousing implementation project participants to determine what aspects they perceived should contribute to the implementation process. The respondents included: functional managers/staff, IS managers/staff, and consultants. The study identified eight significant factors that participants perceived should impact data warehouse implementation. [ABSTRACT FROM AUTHOR]

Case Base Reasoning (CBR), which is characterized by its capability to capture past experience and knowledge for case matching in various applications, is an emerging and well-accepted approach in the implementation Knowledge Management (KM) systems. The data format of CBR belongs to the ‘free’ type and therefore is dissimilar to the traditional relational data model which emphasizes on specified data fields, field lengths and data types. However, there is a lack of research regarding the seamless integration of these heterogeneous data models for achieving effective data communication, which is essential to enhance business workflow of enterprises. This paper attempts to propose an integrated knowledge system to support the extrapolation of projected outcomes of events based on knowledge generated by the relational database model and CBR knowledge model, both of which supplement and complement each other by virtue of their distinct structural features. [Copyright &y& Elsevier]

This article reports on an implementation study of a computerised decision support system for public transport management for the Athens Public Transport Authority (APTA). The study analysed the current situation, specified user requirements, determined system functionality, designed the system architecture, organised the project and, finally, planned dissemination activities. The decision support system covers the main public transport management activities of APTA - transport service provision, economic and financial planning, passenger complaints and suggestions, and personnel management - offering its users the possibility to study the potential effects of their decisions before implementation. To achieve this, the system will consist of a data warehousing system, for the integrated management of a wide variety of data sources, and of online analytical processing tools, for information analysis and scenario testing. The system architecture has been designed to offer robustness, flexibility, rich functionality and open connectivity. The system aims to help APTA reduce operational costs and provide a better transport service, attracting more passengers to public transport and reducing, in the process, congestion and pollution. [ABSTRACT FROM AUTHOR]

This paper presents the results of industrial work on the logical integration of Online Analytical Processing (OLAP) and eXtensible Markup Language (XML) data sources, carried out in cooperation between TARGIT, a Danish OLAP client vendor, and Aalborg University. A prototype has been developed that allows XML data stored outside the OLAP system to be used as dimensions and measures in the OLAP system in the same way as ordinary dimensions and measures. This provides a powerful and flexible way to handle unexpected or short-term data requirements as well as rapidly changing data. Compared to earlier work, this paper presents several major extensions that resulted from TARGIT'S requirements. These include the ability to use XML data as measures, as well as a novel multigranular data model and query language that formalises and extends the TARGIT data model and query language. Experiments indicate that our approach is often a feasible alternative to physical integration. [ABSTRACT FROM AUTHOR]

Commercial OLAP systems usually consider OLAP dimensions as static entities. In practice, dimension updates are often necessary in order to adapt the multidimensional database to changing requirements. Query languages are then needed to retrieve historical information from a data warehouse. Although many proposals addressing temporal OLAP exist, none supports full operation over the World Wide Web. After introducing a temporal data model supporting historical dimensions and fact table versioning, we present a three-tier architecture based on web services, SOAP and XML, allowing efficient querying over the web. In this architecture, XML metadata is stored at the application server in the form of XML documents containing the data warehouse structure. This allows addressing most of the requests (navigation through time, updates and queries) without accessing the data warehouse. In the second part of the paper, we present TOLAP-QL, an SQL-like temporal query language. We describe the syntax and semantics of the language, a sequel of its logic-based predecessor, TOLAP. We study query optimisation in temporal OLAP, and introduce a technique based on materialisation of historical views. Last, but not least, we present a system implementation, and a real-world case study, that shows that our proposal achieves good query performance for OLAP queries over large historical databases in a web environment. [ABSTRACT FROM AUTHOR]

With the growing popularity of mobile computing and the empowerment of mobile devices by high end-users and decision-makers, the development of architectures based on web services for the deployment of mobile Spatial OLAP (SOLAP) applications is being perceived as a powerful solution. This paper deals with geospatial (web) Services-Oriented Architectures (SOA) for mobile processing of geo-decisional information. The paper first reviews the underlying concepts of typical SOLAP applications as well as the general concepts of mobile computing. The existing SOLAP functionalities are presented and their viability/usefulness in a mobile environment are discussed. Different web services based on an extended geospatial SOA are then proposed in order to tackle the several issues related to mobile SOLAP. Finally, several research challenges and outlooks are discussed. [ABSTRACT FROM AUTHOR]

Data warehousing and Online Analytical Processing (OLAP) technologies are now moving onto handling complex data that mostly originate from the web. However, integrating such data into a decision-support process requires their representation in a form processable by OLAP and/or data mining techniques. We present in this paper a complex data warehousing methodology that exploits extensible Markup Language (XML) as a pivot language. Our approach includes the integration of complex data in an ODS, in the form of XML documents; their dimensional modelling and storage in an XML data warehouse; and their analysis with combined OLAP and data mining techniques. We also address the crucial issue of performance in XML warehouses. [ABSTRACT FROM AUTHOR]

Third Party Logistics (3PL) becomes more important nowadays as it aims to improve supply chain efficiency through reducing cycle time and inventory level for both upstream suppliers and downstream retailers. However, many small to medium-sized 3PL providers in the Pearl River Delta (PRD) region still highly rely on manual processes, which make them difficult to achieve the role of supply chain facilitators. In this study, a logistics information hub named Integrated Logistics Information Management System (ILIMS) adopting Radio Frequency Identification (RFID) technology is presented to suit this need. With ILIMS, 3PL companies are provided with unified business processes with enhanced tracking capability. A case study is discussed in applying ILIMS to a medium-sized 3PL company. Through using this generic system, the logistics visibility in the supply chain is improved with significant reduction of delivery time, supply cost, inventory and out-of-stock frequency that lead to increase of revenue and operations volume. [ABSTRACT FROM AUTHOR]