Benchmarking Summarizability Processing in XML Warehouses with Complex Hierarchies

Business Intelligence plays an important role in decision making. Based on data warehouses and Online Analytical Processing, a business intelligence tool can be used to analyze complex data. Still, summarizability issues in data warehouses cause ineffective analyses that may become critical problems to businesses. To settle this issue, many researchers have studied and proposed various solutions, both in relational and XML data warehouses. However, they find difficulty in evaluating the performance of their proposals since the available benchmarks lack complex hierarchies. In order to contribute to summarizability analysis, this paper proposes an extension to the XML warehouse benchmark (XWeB) with complex hierarchies. The benchmark enables us to generate XML data warehouses with scalable complex hierarchies as well as summarizability processing. We experimentally demonstrated that complex hierarchies can definitely be included into a benchmark dataset, and that our benchmark is able to compare two alternative approaches dealing with summarizability issues.Comment: 15th International Workshop on Data Warehousing and OLAP (DOLAP 2012), Maui : United States (2012

Representation of Aggregation Knowledge in OLAP Systems

Decision support systems are mainly based on multidimensional modeling. Using On-Line Analytical Processing (OLAP) tools, decision makers navigate through and analyze multidimensional data. Typically, users need to analyze data at different aggregation levels, using OLAP operators such as roll-up and drill-down. Roll-up operators decrease the details of the measure, aggregating it along the dimension hierarchy. Conversely, drill-down operators increase the details of the measure. As a consequence, dimensions hierarchies play a central role in knowledge representation. More precisely, since aggregation hierarchies are widely used to support data aggregation, aggregation knowledge should be adequately represented in conceptual multidimensional models, and mapped in subsequent logical and physical models. However, current conceptual multidimensional models poorly represent aggregation knowledge, which (1) has a complex structure and dynamics and (2) is highly contextual. In order to account for the characteristics of this knowledge, we propose to represent it with objects and rules. Static aggregation knowledge is represented using UML class diagrams, while rules, which represent the dynamics (i.e. how aggregation may be performed depending on context), are represented using the Production Rule Representation (PRR) language. The latter allows us to incorporate dynamic aggregation knowledge. We argue that this representation of aggregation knowledge allows an early modeling of user requirements in a decision support system project. In order to illustrate the applicability and benefits of our approach, we exemplify the production rules and present an application scenario

COVID-19 Data Warehouse: A Systematic Literature Review

The coronavirus disease (COVID-19) affects the whole world and led clinicians to use the available knowledge to diagnose or predict the infection. Data Warehouse is one of the most crucial tools that may enhance decision-making (DW).In this paper, three main questions will be investigated according to using DW in the COVID-19 pandemic. The effect of using DW in the field of diagnosing and prediction will be investigated, besides, the most used architecture of DW will be explored. The sectors that faced a lot of researchers' attention such as diagnosing, predicting, and finding the correlations among features will be examined. The selected studies are explored where the papers that have been published between 2019-2022 in the digital libraries (ACM, IEEE, Springer, Science Direct, and Elsevier) in the field of DW that handle the COVID-19 are selected. During the research, many limitations have been detected, while some future works are presented. Enterprise DW is the most used architecture for COVID-19 DW while finding correlation among features and prediction are the sectors that had taken the researchers' attentio

Data Sharing in P2P Systems

To appear in Springer's "Handbook of P2P Networking"In this chapter, we survey P2P data sharing systems. All along, we focus on the evolution from simple file-sharing systems, with limited functionalities, to Peer Data Management Systems (PDMS) that support advanced applications with more sophisticated data management techniques. Advanced P2P applications are dealing with semantically rich data (e.g. XML documents, relational tables), using a high-level SQL-like query language. We start our survey with an overview over the existing P2P network architectures, and the associated routing protocols. Then, we discuss data indexing techniques based on their distribution degree and the semantics they can capture from the underlying data. We also discuss schema management techniques which allow integrating heterogeneous data. We conclude by discussing the techniques proposed for processing complex queries (e.g. range and join queries). Complex query facilities are necessary for advanced applications which require a high level of search expressiveness. This last part shows the lack of querying techniques that allow for an approximate query answering

Trade-off among timeliness, messages and accuracy for large-Ssale information management

The increasing amount of data and the number of nodes in large-scale environments require new techniques for information management. Examples of such environments are the decentralized infrastructures of Computational Grid and Computational Cloud applications. These large-scale applications need different kinds of aggregated information such as resource monitoring, resource discovery or economic information. The challenge of providing timely and accurate information in large scale environments arise from the distribution of the information. Reasons for delays in distributed information system are a long information transmission time due to the distribution, churn and failures. A problem of large applications such as peer-to-peer (P2P) systems is the increasing retrieval time of the information due to the decentralization of the data and the failure proneness. However, many applications need a timely information provision. Another problem is an increasing network consumption when the application scales to millions of users and data. Using approximation techniques allows reducing the retrieval time and the network consumption. However, the usage of approximation techniques decreases the accuracy of the results. Thus, the remaining problem is to offer a trade-off in order to solve the conflicting requirements of fast information retrieval, accurate results and low messaging cost. Our goal is to reach a self-adaptive decision mechanism to offer a trade-off among the retrieval time, the network consumption and the accuracy of the result. Self-adaption enables distributed software to modify its behavior based on changes in the operating environment. In large-scale information systems that use hierarchical data aggregation, we apply self-adaptation to control the approximation used for the information retrieval and reduces the network consumption and the retrieval time. The hypothesis of the thesis is that approximation techniquescan reduce the retrieval time and the network consumption while guaranteeing an accuracy of the results, while considering user’s defined priorities. First, this presented research addresses the problem of a trade-off among a timely information retrieval, accurate results and low messaging cost by proposing a summarization algorithm for resource discovery in P2P-content networks. After identifying how summarization can improve the discovery process, we propose an algorithm which uses a precision-recall metric to compare the accuracy and to offer a user-driven trade-off. Second, we propose an algorithm that applies a self-adaptive decision making on each node. The decision is about the pruning of the query and returning the result instead of continuing the query. The pruning reduces the retrieval time and the network consumption at the cost of a lower accuracy in contrast to continuing the query. The algorithm uses an analytic hierarchy process to assess the user’s priorities and to propose a trade-off in order to satisfy the accuracy requirements with a low message cost and a short delay. A quantitative analysis evaluates our presented algorithms with a simulator, which is fed with real data of a network topology and the nodes’ attributes. The usage of a simulator instead of the prototype allows the evaluation in a large scale of several thousands of nodes. The algorithm for content summarization is evaluated with half a million of resources and with different query types. The selfadaptive algorithm is evaluated with a simulator of several thousands of nodes that are created from real data. A qualitative analysis addresses the integration of the simulator’s components in existing market frameworks for Computational Grid and Cloud applications. The proposed content summarization algorithm reduces the information retrieval time from a logarithmic increase to a constant factor. Furthermore, the message size is reduced significantly by applying the summarization technique. For the user, a precision-recall metric allows defining the relation between the retrieval time and the accuracy. The self-adaptive algorithm reduces the number of messages needed from an exponential increase to a constant factor. At the same time, the retrieval time is reduced to a constant factor under an increasing number of nodes. Finally, the algorithm delivers the data with the required accuracy adjusting the depth of the query according to the network conditions.La gestió de la informació exigeix noves tècniques que tractin amb la creixent quantitat de dades i nodes en entorns a gran escala. Alguns exemples d’aquests entorns són les infraestructures descentralitzades de Computacional Grid i Cloud. Les aplicacions a gran escala necessiten diferents classes d’informació agregada com monitorització de recursos i informació econòmica. El desafiament de proporcionar una provisió ràpida i acurada d’informació en ambients de grans escala sorgeix de la distribució de la informació. Una raó és que el sistema d’informació ha de tractar amb l’adaptabilitat i fracassos d’aquests ambients. Un problema amb aplicacions molt grans com en sistemes peer-to-peer (P2P) és el creixent temps de recuperació de l’informació a causa de la descentralització de les dades i la facilitat al fracàs. No obstant això, moltes aplicacions necessiten una provisió d’informació puntual. A més, alguns usuaris i aplicacions accepten inexactituds dels resultats si la informació es reparteix a temps. A més i més, el consum de xarxa creixent fa que sorgeixi un altre problema per l’escalabilitat del sistema. La utilització de tècniques d’aproximació permet reduir el temps de recuperació i el consum de xarxa. No obstant això, l’ús de tècniques d’aproximació disminueix la precisió dels resultats. Així, el problema restant és oferir un compromís per resoldre els requisits en conflicte d’extracció de la informació ràpida, resultats acurats i cost d’enviament baix. El nostre objectiu és obtenir un mecanisme de decisió completament autoadaptatiu per tal d’oferir el compromís entre temps de recuperació, consum de xarxa i precisió del resultat. Autoadaptacío permet al programari distribuït modificar el seu comportament en funció dels canvis a l’entorn d’operació. En sistemes d’informació de gran escala que utilitzen agregació de dades jeràrquica, l’auto-adaptació permet controlar l’aproximació utilitzada per a l’extracció de la informació i redueixen el consum de xarxa i el temps de recuperació. La hipòtesi principal d’aquesta tesi és que els tècniques d’aproximació permeten reduir el temps de recuperació i el consum de xarxa mentre es garanteix una precisió adequada definida per l’usari. La recerca que es presenta, introdueix un algoritme de sumarització de continguts per a la descoberta de recursos a xarxes de contingut P2P. Després d’identificar com sumarització pot millorar el procés de descoberta, proposem una mètrica que s’utilitza per comparar la precisió i oferir un compromís definit per l’usuari. Després, introduïm un algoritme nou que aplica l’auto-adaptació a un ordre per satisfer els requisits de precisió amb un cost de missatge baix i un retard curt. Basat en les prioritats d’usuari, l’algoritme troba automàticament un compromís. L’anàlisi quantitativa avalua els algoritmes presentats amb un simulador per permetre l’evacuació d’uns quants milers de nodes. El simulador s’alimenta amb dades d’una topologia de xarxa i uns atributs dels nodes reals. L’algoritme de sumarització de contingut s’avalua amb mig milió de recursos i amb diferents tipus de sol·licituds. L’anàlisi qualitativa avalua la integració del components del simulador en estructures de mercat existents per a aplicacions de Computacional Grid i Cloud. Així, la funcionalitat implementada del simulador (com el procés d’agregació i la query language) és comprovada per la integració de prototips. L’algoritme de sumarització de contingut proposat redueix el temps d’extracció de l’informació d’un augment logarítmic a un factor constant. A més, també permet que la mida del missatge es redueix significativament. Per a l’usuari, una precision-recall mètric permet definir la relació entre el nivell de precisió i el temps d’extracció de la informació. Alhora, el temps de recuperació es redueix a un factor constant sota un nombre creixent de nodes. Finalment, l’algoritme reparteix les dades amb la precisió exigida i ajusta la profunditat de la sol·licitud segons les condicions de xarxa. Els algoritmes introduïts són prometedors per ser utilitzats per l’agregació d’informació en nous sistemes de gestió de la informació de gran escala en el futur

Trade-off among timeliness, messages and accuracy for large-Ssale information management

The increasing amount of data and the number of nodes in large-scale environments require new techniques for information management. Examples of such environments are the decentralized infrastructures of Computational Grid and Computational Cloud applications. These large-scale applications need different kinds of aggregated information such as resource monitoring, resource discovery or economic information. The challenge of providing timely and accurate information in large scale environments arise from the distribution of the information. Reasons for delays in distributed information system are a long information transmission time due to the distribution, churn and failures. A problem of large applications such as peer-to-peer (P2P) systems is the increasing retrieval time of the information due to the decentralization of the data and the failure proneness. However, many applications need a timely information provision. Another problem is an increasing network consumption when the application scales to millions of users and data. Using approximation techniques allows reducing the retrieval time and the network consumption. However, the usage of approximation techniques decreases the accuracy of the results. Thus, the remaining problem is to offer a trade-off in order to solve the conflicting requirements of fast information retrieval, accurate results and low messaging cost. Our goal is to reach a self-adaptive decision mechanism to offer a trade-off among the retrieval time, the network consumption and the accuracy of the result. Self-adaption enables distributed software to modify its behavior based on changes in the operating environment. In large-scale information systems that use hierarchical data aggregation, we apply self-adaptation to control the approximation used for the information retrieval and reduces the network consumption and the retrieval time. The hypothesis of the thesis is that approximation techniquescan reduce the retrieval time and the network consumption while guaranteeing an accuracy of the results, while considering user’s defined priorities. First, this presented research addresses the problem of a trade-off among a timely information retrieval, accurate results and low messaging cost by proposing a summarization algorithm for resource discovery in P2P-content networks. After identifying how summarization can improve the discovery process, we propose an algorithm which uses a precision-recall metric to compare the accuracy and to offer a user-driven trade-off. Second, we propose an algorithm that applies a self-adaptive decision making on each node. The decision is about the pruning of the query and returning the result instead of continuing the query. The pruning reduces the retrieval time and the network consumption at the cost of a lower accuracy in contrast to continuing the query. The algorithm uses an analytic hierarchy process to assess the user’s priorities and to propose a trade-off in order to satisfy the accuracy requirements with a low message cost and a short delay. A quantitative analysis evaluates our presented algorithms with a simulator, which is fed with real data of a network topology and the nodes’ attributes. The usage of a simulator instead of the prototype allows the evaluation in a large scale of several thousands of nodes. The algorithm for content summarization is evaluated with half a million of resources and with different query types. The selfadaptive algorithm is evaluated with a simulator of several thousands of nodes that are created from real data. A qualitative analysis addresses the integration of the simulator’s components in existing market frameworks for Computational Grid and Cloud applications. The proposed content summarization algorithm reduces the information retrieval time from a logarithmic increase to a constant factor. Furthermore, the message size is reduced significantly by applying the summarization technique. For the user, a precision-recall metric allows defining the relation between the retrieval time and the accuracy. The self-adaptive algorithm reduces the number of messages needed from an exponential increase to a constant factor. At the same time, the retrieval time is reduced to a constant factor under an increasing number of nodes. Finally, the algorithm delivers the data with the required accuracy adjusting the depth of the query according to the network conditions.La gestió de la informació exigeix noves tècniques que tractin amb la creixent quantitat de dades i nodes en entorns a gran escala. Alguns exemples d’aquests entorns són les infraestructures descentralitzades de Computacional Grid i Cloud. Les aplicacions a gran escala necessiten diferents classes d’informació agregada com monitorització de recursos i informació econòmica. El desafiament de proporcionar una provisió ràpida i acurada d’informació en ambients de grans escala sorgeix de la distribució de la informació. Una raó és que el sistema d’informació ha de tractar amb l’adaptabilitat i fracassos d’aquests ambients. Un problema amb aplicacions molt grans com en sistemes peer-to-peer (P2P) és el creixent temps de recuperació de l’informació a causa de la descentralització de les dades i la facilitat al fracàs. No obstant això, moltes aplicacions necessiten una provisió d’informació puntual. A més, alguns usuaris i aplicacions accepten inexactituds dels resultats si la informació es reparteix a temps. A més i més, el consum de xarxa creixent fa que sorgeixi un altre problema per l’escalabilitat del sistema. La utilització de tècniques d’aproximació permet reduir el temps de recuperació i el consum de xarxa. No obstant això, l’ús de tècniques d’aproximació disminueix la precisió dels resultats. Així, el problema restant és oferir un compromís per resoldre els requisits en conflicte d’extracció de la informació ràpida, resultats acurats i cost d’enviament baix. El nostre objectiu és obtenir un mecanisme de decisió completament autoadaptatiu per tal d’oferir el compromís entre temps de recuperació, consum de xarxa i precisió del resultat. Autoadaptacío permet al programari distribuït modificar el seu comportament en funció dels canvis a l’entorn d’operació. En sistemes d’informació de gran escala que utilitzen agregació de dades jeràrquica, l’auto-adaptació permet controlar l’aproximació utilitzada per a l’extracció de la informació i redueixen el consum de xarxa i el temps de recuperació. La hipòtesi principal d’aquesta tesi és que els tècniques d’aproximació permeten reduir el temps de recuperació i el consum de xarxa mentre es garanteix una precisió adequada definida per l’usari. La recerca que es presenta, introdueix un algoritme de sumarització de continguts per a la descoberta de recursos a xarxes de contingut P2P. Després d’identificar com sumarització pot millorar el procés de descoberta, proposem una mètrica que s’utilitza per comparar la precisió i oferir un compromís definit per l’usuari. Després, introduïm un algoritme nou que aplica l’auto-adaptació a un ordre per satisfer els requisits de precisió amb un cost de missatge baix i un retard curt. Basat en les prioritats d’usuari, l’algoritme troba automàticament un compromís. L’anàlisi quantitativa avalua els algoritmes presentats amb un simulador per permetre l’evacuació d’uns quants milers de nodes. El simulador s’alimenta amb dades d’una topologia de xarxa i uns atributs dels nodes reals. L’algoritme de sumarització de contingut s’avalua amb mig milió de recursos i amb diferents tipus de sol·licituds. L’anàlisi qualitativa avalua la integració del components del simulador en estructures de mercat existents per a aplicacions de Computacional Grid i Cloud. Així, la funcionalitat implementada del simulador (com el procés d’agregació i la query language) és comprovada per la integració de prototips. L’algoritme de sumarització de contingut proposat redueix el temps d’extracció de l’informació d’un augment logarítmic a un factor constant. A més, també permet que la mida del missatge es redueix significativament. Per a l’usuari, una precision-recall mètric permet definir la relació entre el nivell de precisió i el temps d’extracció de la informació. Alhora, el temps de recuperació es redueix a un factor constant sota un nombre creixent de nodes. Finalment, l’algoritme reparteix les dades amb la precisió exigida i ajusta la profunditat de la sol·licitud segons les condicions de xarxa. Els algoritmes introduïts són prometedors per ser utilitzats per l’agregació d’informació en nous sistemes de gestió de la informació de gran escala en el futur.Postprint (published version

Mining climate data for shire level wheat yield predictions in Western Australia

Climate change and the reduction of available agricultural land are two of the most important factors that affect global food production especially in terms of wheat stores. An ever increasing world population places a huge demand on these resources. Consequently, there is a dire need to optimise food production. Estimations of crop yield for the South West agricultural region of Western Australia have usually been based on statistical analyses by the Department of Agriculture and Food in Western Australia. Their estimations involve a system of crop planting recommendations and yield prediction tools based on crop variety trials. However, many crop failures arise from adherence to these crop recommendations by farmers that were contrary to the reported estimations. Consequently, the Department has sought to investigate new avenues for analyses that improve their estimations and recommendations. This thesis explores a new approach in the way analyses are carried out. This is done through the introduction of new methods of analyses such as data mining and online analytical processing in the strategy. Additionally, this research attempts to provide a better understanding of the effects of both gradual variation parameters such as soil type, and continuous variation parameters such as rainfall and temperature, on the wheat yields. The ultimate aim of the research is to enhance the prediction efficiency of wheat yields. The task was formidable due to the complex and dichotomous mixture of gradual and continuous variability data that required successive information transformations. It necessitated the progressive moulding of the data into useful information, practical knowledge and effective industry practices. Ultimately, this new direction is to improve the crop predictions and to thereby reduce crop failures. The research journey involved data exploration, grappling with the complexity of Geographic Information System (GIS), discovering and learning data compatible software tools, and forging an effective processing method through an iterative cycle of action research experimentation. A series of trials was conducted to determine the combined effects of rainfall and temperature variations on wheat crop yields. These experiments specifically related to the South Western Agricultural region of Western Australia. The study focused on wheat producing shires within the study area. The investigations involved a combination of macro and micro analyses techniques for visual data mining and data mining classification techniques, respectively. The research activities revealed that wheat yield was most dependent upon rainfall and temperature. In addition, it showed that rainfall cyclically affected the temperature and soil type due to the moisture retention of crop growing locations. Results from the regression analyses, showed that the statistical prediction of wheat yields from historical data, may be enhanced by data mining techniques including classification. The main contribution to knowledge as a consequence of this research was the provision of an alternate and supplementary method of wheat crop prediction within the study area. Another contribution was the division of the study area into a GIS surface grid of 100 hectare cells upon which the interpolated data was projected. Furthermore, the proposed framework within this thesis offers other researchers, with similarly structured complex data, the benefits of a general processing pathway to enable them to navigate their own investigations through variegated analytical exploration spaces. In addition, it offers insights and suggestions for future directions in other contextual research explorations

Biomedical data integration in computational drug design and bioinformatics

[Abstract In recent years, in the post genomic era, more and more data is being generated by biological high throughput technologies, such as proteomics and transcriptomics. This omics data can be very useful, but the real challenge is to analyze all this data, as a whole, after integrating it. Biomedical data integration enables making queries to different, heterogeneous and distributed biomedical data sources. Data integration solutions can be very useful not only in the context of drug design, but also in biomedical information retrieval, clinical diagnosis, system biology, etc. In this review, we analyze the most common approaches to biomedical data integration, such as federated databases, data warehousing, multi-agent systems and semantic technology, as well as the solutions developed using these approaches in the past few years.Red Gallega de Investigación sobre Cáncer Colorrectal; Ref. 2009/58Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo; 209RT- 0366Instituto de Salud Carlos III; PIO52048Instituto de Salud Carlos III; RD07/0067/0005Ministerio de Industria, Turismo y Comercio; TSI-020110-2009-

Using data analysis and Information visualization techniques to support the effective analysis of large financial data sets

There have been a number of technological advances in the last ten years, which has resulted in the amount of data generated in organisations increasing by more than 200% during this period. This rapid increase in data means that if financial institutions are to derive significant value from this data, they need to identify new ways to analyse this data effectively. Due to the considerable size of the data, financial institutions also need to consider how to effectively visualise the data. Traditional tools such as relational database management systems have problems processing large amounts of data due to memory constraints, latency issues and the presence of both structured and unstructured data The aim of this research was to use data analysis and information visualisation techniques (IV) to support the effective analysis of large financial data sets. In order to visually analyse the data effectively, the underlying data model must produce results that are reliable. A large financial data set was identified, and used to demonstrate that IV techniques can be used to support the effective analysis of large financial data sets. A review of the literature on large financial data sets, visual analytics, existing data management and data visualisation tools identified the shortcomings of existing tools. This resulted in the determination of the requirements for the data management tool, and the IV tool. The data management tool identified was a data warehouse and the IV toolkit identified was Tableau. The IV techniques identified included the Overview, Dashboards and Colour Blending. The IV tool was implemented and published online and can be accessed through a web browser interface. The data warehouse and the IV tool were evaluated to determine their accuracy and effectiveness in supporting the effective analysis of the large financial data set. The experiment used to evaluate the data warehouse yielded positive results, showing that only about 4% of the records had incorrect data. The results of the user study were positive and no major usability issues were identified. The participants found the IV techniques effective for analysing the large financial data set