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Background: The widespread adoption of the electronic health record (EHR) has resulted in vast repositories of EHR big data that are being used to identify patterns and correlations that translate into data-informed health care decision making. Problem: Health care professionals need the skills necessary to navigate a digitized, data-rich health care environment as big data plays an increasingly integral role in health care. Approach: Faculty incorporated the concept of big data in an asynchronous online course allowing an interprofessional mix of students to analyze EHR big data on over a million patients. Outcomes: Students conducted a descriptive analysis of cohorts of patients with selected diagnoses and presented their findings. Conclusions: Students collaborated with an interprofessional team to analyze EHR big data on selected variables. The teams used data visualization tools to describe an assigned diagnosis patient population. [ABSTRACT FROM AUTHOR]

Hive can store massive data through extended clusters, far exceeding the expansion and storage capabilities of traditional databases, and has become a major tool for building data warehouses in the era of big data. Based on Hive technology, this paper studies the enterprise-level data warehouse architecture composed of data storage layer, Hive data warehouse layer and application layer. Then, the paper studies data warehouse tools, uses HDFS for underlying storage and MapReduce for computing engine, and compares Hive data warehouse with relational database. Then, the ETL process is studied. The data goes through three stages: extraction, conversion and loading, and finally the data flows from the source to the target end. Finally, the system function and test are studied, the subsystems including data processing, data management, data migration and data analysis are constructed, and the core functions are tested. The test results were consistent with the expected results and met the delivery standards. [ABSTRACT FROM AUTHOR]

Against the backdrop of the rapid development of information technology, the total amount of data has exploded, and efficient association rule mining methods for large-scale datasets have been studied. Conventional rule mining algorithms are subject to electrical constraints when working, and their convergence speed and data noise are currently the main problems they face. In order to accelerate the working process of the algorithm, this study introduces a data warehouse into the K-Means algorithm. The time series and voltage interaction functions are connected with the long-and-short-term memory network for efficient information analysis of power grid data, generating fusion algorithms. When mining association rules in electrical data sets, the pruning strategy is used to effectively reduce the search space, thus improving the efficiency of the algorithm. The pruning strategy applied in this study is confidence pruning, and the rules that do not meet the conditions are deleted by calculating confidence. For large-scale data sets, the distributed computing framework can dispatch tasks to multiple computing nodes, thus accelerating the computing speed. In the voltage interaction experiment, the storage and processing cost of electrical data is very high, so the research on using compression technology to reduce the dimension of storage space will reduce the computational complexity of the algorithm. The study conducts experiments on the Netloss dataset and three models, including long-and-short-term memory networks, to verify the superiority of the fusion algorithm. Under the same experimental voltage, the circuit power flows of the four models were 0.37, 0.64, 0.79, and 0.82A, respectively, indicating that the algorithm effectively controlled the electrical dataset. Its measurement accuracy was the highest among the four models, at 91.7%. The experimental results showed that the fusion algorithm proposed in the study had precise control ability in power grid datasets, and effectively mined association rules on large-scale datasets. This paper proposes a novel approach for efficient association rule mining in large-scale power grid datasets. A fusion algorithm is developed combining clustering, neural networks, and association rule learning. The technique incorporates data warehousing, time series modeling, and voltage interaction analysis to enhance information extraction from electrical data. Experiments on the Netloss dataset demonstrate the algorithm's effectiveness for power flow control and measurement accuracy compared to standard methods. Results show significant improvements in active power loss and network loss metrics as well. This research provides an important foundation for scalable analytics in smart power systems. [ABSTRACT FROM AUTHOR]

Over the past two decades, the way computing resources are been developed, deployed, upgraded, and applied changed dramatically, with more and more software and hardware solutions being transferred to cloud technologies. Data Warehouses (DW), defined as a way of organizing corporate data in an integrated manner over (sequential) time periods, "structured & disposed" in order to generate a "single data source", were also affected by the evolution, thus giving rise to the concept of Cloud Data Warehouse (CDW). This technology allows users to be more technologically free, as they do not need to spend time investing in software and hardware, they only pay for the resources they used and the infrastructure itself has greater flexibility and scalability. However, selecting the most suitable platform or technology for a CDW can be a complex task due to the large number of factors that can influence the decision and due to the existing offer in the market. The objective of this paper is to describe the process of benchmarking a set of CDW platforms, with the goal of analyzing and exposing each one's performance results. These platforms are Snowflake, Google BigQuery, Amazon Redshift, and Azure Synapse. The metrics to be measured are data loading and query running time, and alias running times. For this benchmark, the dataset used was Star Schema Benchmark (SSB), a dataset based on the well-known TPC Benchmark™ H (TPC-H). [ABSTRACT FROM AUTHOR]

Knowing that nowadays information is one of the strongest weapons a company can have, it has a relevant role in the decision-making part of the company. Building the right structures and using the right tools for these structures is essential for a certain company or business to be a leader in the field where it operates. Building IT infrastructure in business is a must for businesses today, one of the most innovative areas today is undoubtedly Business Intelligence. BI has restructured the company's decision-making processes, becoming the main pillar on which decision-making is based. This paper sheds light on the main concepts related to BI, also it focuses on the main techniques of Business Intelligence, BI techniques, and analysis of the current situation of use and involvement of BI in Albanian companies. [ABSTRACT FROM AUTHOR]

Healthcare facilities use huge quantities of real-time and analytical data to discover meaningful information from patient clinical lab results. Advanced analytics and machine learning algorithms help doctors identify and treat patients more accurately. Accurate models must be trained, tested, and validated with enough data. New real-time data allows healthcare practitioners to quickly and accurately analyse patient demands. Healthcare organizations can improve patient care and outcomes through knowledge discovery. The goal of this effort is to develop a real-time data repository based on patient clinical exams. This collection feeds real-time monitoring panels and machine or deep learning algorithms that forecast patient progression from clinical lab results. Integrate HL7 messages from diverse sources, preprocess them, and add them to an API-accessible data warehouse. In conclusion, the proposed method creates an international-standard data warehouse. This data warehouse can increase healthcare decision-making accuracy and efficacy when utilised with machine learning models, improving patient care and outcomes through more personalised treatment options. [ABSTRACT FROM AUTHOR]

Within the big data phenomenon, cluster computing has attracted special attention for its impressive ability to process a vast amount of data. Hadoop cluster is a promising cluster computing framework for implementing big data warehouses and conducting big data analysis, thanks to its distributed file system and MapReduce paradigm. In this paper, we propose a new data placement strategy for a document-oriented data warehouse within the distributed environment of Hadoop. Our contribution includes formalizing the logical model and cube building operators. First, we present the cube building algorithm's processing using the MapReduce paradigm, and then we explore the possibility of accelerating the process by using Spark instead. To evaluate the proposed framework in terms of OLAP cube construction cost, we conducted experiments on a physical cluster, which yielded promising results, specifically, that the proposed framework enables efficient data placement and significantly speeds up cube building compared to a similar OLAP infrastructure chosen from existing literature. [ABSTRACT FROM AUTHOR]

The digital age is responsible for the continuous growth of data volume. Everything related to Information and Communication Technologies contributes to the exponential growth of data, such as social networks, financial technology, and scientific data. Data Mining is a method of extracting data from systems such as Business Intelligence, Big Data, Data Warehouse, so that the patterns extracted during the process are considered information, knowledge, in relation to the theoretical concepts that support them. In this sense, these patterns should be presented as representations of knowledge. In the test case presented, we observed the various phases of the knowledge discovery process and analysed data using regression and classification models, to support companies in the knowledge management. [ABSTRACT FROM AUTHOR]

New technologies and the use of the Internet of Things (IoT) play a remarkable role in the design, construction, and maintenance of Smart Cities. With the exponential increase of data generated in the areas of health, commerce, financial services, science, education, public administration, and society, the challenge of ensuring data security and quality is increasing. IoT and Smart Cities, are in permanent mutation and evolution, towards a new paradigm of digitalization and digital transformation. The existing literature review exposes much of the attention that is given to IoT, but a broad discussion on the characteristics, use, applications, and challenges of these technologies in Smart Cities is still needed. The main contribution of this paper is that it provides a systematic assessment and synthesis of the critical applications and challenges of IoT, in the development of Smart Cities. [ABSTRACT FROM AUTHOR]

The concept of data lake was proposed more than a decade ago. Although progress has been made in data lake research and applications, there are also numerous issues and challenges need to be addressed. In this paper, we survey some recent developments, provide our observations, as well as our expectations on future research and practice in this area. We start with a discussion on general terminology in data lakes, then review various aspects of data lakes, including an examination on metadata and related issues, information granularities involved in data lakes, unique features such as mixed lazy and eager approach in data lake lifecycle, relationship between data lakes and data mining, interplay between data lakes and data warehousing techniques, as well as some notable developments related to applications of data lakes. We present our observations while we examine these aspects, and wrap up this paper by expressing our hope of developing a unified framework for data lake lifecycle evaluation. [ABSTRACT FROM AUTHOR]

In this paper, we propose a multidimensional spatiotemporal modeling framework of data warehouse creation for tracing dynamic events in contemporary applications, like crowd contact tracing for Covid-19 prevention. Such a framework offers a natural and consistent solution for slowly changing dimension management. It provides a progressive evolution from traditional static data management to modem dynamic data analysis with spatiotemporal tracking capabilities for IoT applications. Based on such a framework, entity-centered resource integration and related business intelligence applications can be rig· orously developed, managed and properly tracked. [ABSTRACT FROM AUTHOR]

SAP HANA is an innovative in-memory enterprise data platform with advanced data warehouse characteristics. Many DBMS vendors have recently updated their products with data-in-memory and columnar data storage features in their new versions. To catch the database's industrial trend, the author has introduced SAP HANA technology into the database and data warehousing courses using SAP University Alliances curriculum materials. The author has taught SAP HANA for data warehousing in her Database and Data Warehouse Systems course for over three years. SAP HANA course materials are downloaded from SAP University Alliances Learning Portal because the Data Warehouse Chapter in the textbook does not cover specific SAP HANA features. The paper presents the importance, framework, and delivery models of integrating SAP HANA technology into this course. The study focuses on the data warehouse architecture, components, data storage, modeling, and implementation. SAP HANA Business Data Warehouse (BW), business intelligence (BI) course materials. Student learning outcomes of SAP HANA are assessed and analyzed. Learning by implementing data housing systems is vital in teaching the next generation of digital business leaders. The student engagement with hands-on activities needs to be on the real-world enterprise data platform. [ABSTRACT FROM AUTHOR]

The Portuguese Foundation for Science and Technology (FCT) has endorsed Plan-S since early 2021 and it is working towards implementing its obligations. To ensure an effective fulfillment of its open science policies, FCT is implementing a new monitoring and compliance tool, that will enforce the compliance with Plan-S as well as the new FCT's open access (OA) policy. This paper details FCT's approach to implement this tool and discusses the challenges of integrating information that is spread across multiple systems. The new tool will be built on top of infrastructures and services developed by FCT over the last years under the umbrella of the Portuguese Current Research Information System (PTCRIS) program. This tool comprises an operational component, integrated with the grant management system, and an analytical component, based on a Data Warehouse and Business Intelligence (DW/BI) system. The DW/BI system for the ecosystem of Science and Technology in Portugal will be developed using Kimball's Dimensional DW architecture, which advocates the incremental design and development of dimensional data marts linked by conforming dimensions. In this approach, data marts contain atomic and summarized data, corresponding to the development of elementary and derived dimensional models. Three data marts will be developed for the subject areas of funding, scientific results and for monitoring and compliance. The last data mart will calculate the compliance indicators for Plan-S as well as for FCT's OA policy. The operational component of the new tool then receives, from the analytical component, the list of scientific results per funding registry along with data regarding compliance. A list of non-compliances for each scientific result will be the basis of a notification module that will inform the principal investigator (PI) and the FCT compliance officer. [ABSTRACT FROM AUTHOR]

Consolidation of the research information improves the quality of data integration, reducing duplicates between systems and enabling the required flexibility and scalability when processing various data sources. We assume that the combination of a data lake as a data repository and a data wrangling process should allow low-quality or "bad" data to be identified and eliminated, leaving only high-quality data, referred to as "research information" in the Research Information System (RIS) domain, allowing for the most accurate insights gained on their basis. This, however, would lead to increased value of both the data themselves and data-driven actions contributing to more accurate and aware decision-making. This cleansed research information is then entered into the appropriate target Current Research Information System (CRIS) so that it can be used for further data processing steps. In order to minimize the effort for the analysis, the proliferation and enrichment of large amounts of data and metadata, as well as to achieve far-reaching added value in information retrieval for CRIS employees, developers and end users, this paper outlines the concept of a curated data lake with the data wrangling process, showing how it can be used in CRIS to clean up data from heterogeneous data sources during their collection and integration. [ABSTRACT FROM AUTHOR]

Complementary Diagnostic Requests (CDRs) are required for disease identification, monitoring, and prognosis. Diagnostic tests misuse, on the other hand, can lead to negative health outcomes as well as additional costs. Inappropriate diagnostic test requests are primarily the result of a lack of interoperability between Healthcare Information Systems (HIS). On one hand, clinicians can be mislead into which test is the best option for each clinical case, on the other hand missing previous results, leads to duplication or unnecessary tests. HIS is increasingly relying on standards based on dual architecture to promote interoperability as well as the structuring and consistency of clinical and demographic data. The OpenEHR standard's duo-based architecture allows for concise modelling of archetypes and templates for a given clinical case, which was used in this study. As a result, the purpose of this research was to build an openEHR template for the CDR registration as well as the architecture of a Data Warehouse (DW) system capable of storing all of the information needed for the diagnostic test request process. Afterwards, Business Intelligence (BI) indicators was developed in order to answers the needs for test registration and execution. [ABSTRACT FROM AUTHOR]

• Integration Design: Maintains anonymity, immutability, and handles large data. • ETL Rules: New rules enable seamless integration between blockchain and big data. • Sustainable Supply Chain: Integrates traceability and carbon tracking. • Data Storage: Managing large data and complex growth in fisheries supply chains. • Anonymity-Preserving Big Data Storage: Ensures anonymity via blockchain metadata. The utilization of blockchain technology in the fishing industry has been extensively studied and implemented to address issues such as illegal fishing and carbon emissions control. However, integrating blockchain with the vast amounts of data in the fishing supply chain poses significant challenges. Challenges include managing extensive data such as photos or videos for product traceability throughout their lifecycle, compounded by the growing complexity of cross-border trade and market expansion. Additionally, blockchain's storage capacity limitations present hurdles in fully accommodating and comprehensively storing detailed supply data from a complex and expanding supply chain. While solutions like the Interplanetary File System (IPFS) have been explored for large data storage on the blockchain, this paper proposes a directly integrated blockchain solution tailored for the challenges of fishing with big data. We introduce a novel big data design that preserves blockchain's anonymity and immutability features, addressing storage limitations while maintaining the architecture's purpose. Furthermore, our proposal integrates product supply chain traceability with carbon footprint tracking, enabling comprehensive assessment based on quality, sustainability, and carbon footprint criteria. Despite the proposed solution needing to be tested in real-life situations, we conducted rigorous testing through simulation, white-box evaluation, and complexity analysis. The results demonstrate the potential of our solution to address challenges faced in fisheries supply chains, providing valuable insights for future practical implementation and validation efforts. [ABSTRACT FROM AUTHOR]

"Three fit" problems in the Bid Process Information System (BPIS) are still an open issue. The BPIS must be: integrated, flexible and interoperable in order to deal with the urbanization approach. Hence, operational, organizational, decision-making, and cooperative dimensions are four dimensions that have been identified to deal with such failures. Therefore, in this work, we are particularly interested in handling this failure by proposing an approach that focuses on the decision dimension of BPIS. This improves at the end of the various stages of the data warehouse lifecycle and takes into consideration the user's knowledge by focusing on their individual needs too. [ABSTRACT FROM AUTHOR]

The practice of evidence-based medicine has been gaining ground in the most diverse health institutions and consequently in the work of their health professionals. Thus, the Clinical Decision Support Systems appear, which aim to ensure that health professionals are supported with the best evidence in order to improve the clinical decision-making process, reducing the occurrence of adverse events through the availability and sharing of quality information by health professionals. Business Intelligence (BI) and Business Analytics (BA) technologies are increasingly being used to extract knowledge and turn it into useful information and analytical tools for healthcare professionals. This article is part of a dissertation that has as main objective the extraction of knowledge from the hospital data of the obstetrics service in Centro Materno-Infantil do Norte (CMIN) from Centro Hospitalar Universitário do Porto (CHUP) with focus on the area of clinical decision. [ABSTRACT FROM AUTHOR]

The university conducts a tracer study to obtain information about job search and the work situation of alumni after graduating from university. So the university will collect tracer study data from year to year. Therefore, the data integration is carried out so that the use of tracer study alumni data as material for analysis becomes more effective. By using the concept of a data warehouse, it will simplify the process of integrating tracer study data. With the implementation of OLAP technology can generate data analysis reports as a basis for decision-making. The data in data warehouse tracer study will be input data in the OLAP (online analytical processing) application. This study designs a multidimensional schema model with a snowflake schema for a data warehouse tracer study. By determining a sample attribute based on predetermined criteria, it can be a measure for making dimensions. Based on the results of the analysis obtained one fact table and nine dimension tables. [ABSTRACT FROM AUTHOR]

The diagnosis and treatment of breast cancer are very much studied in the field of medical research. Indeed, the early detection of this pathology allows for the best possible treatment and minimization of the after-effects. The business process "Diagnosis of breast cancer in women" is perfectly mastered by the practitioner and the medical profession and is widely documented in the literature. We propose a vision of "analysis of the existing" which allows to discover this process to the computer engineer in charge of the implementation of a system for aid in diagnosis and prognosis of the disease. This step is indispensable to discover the data generated during the business process and to discover in a later step all the characteristics that govern the disease. We define a system of diagnosis and prognosis of the disease Diaprog which uses the data of the classified and archived care records. We say that we use the historical data generated during the main process. We distinguish for this purpose between a production database and a data warehouse. The latter is a database fed from the various production databases accessible and made available in our system. In the case of our work, we call this data warehouse DDA (Diagnosis Data Archive). The data warehouse thus defined is a component of the medical diagnosis aid system Diaprog whose functional architecture also implements a learning module that allows the discovery of relationships between the characteristics that govern the disease. [ABSTRACT FROM AUTHOR]

Background: Electronic health records (EHRs, such as those created by an anesthesia management system) generate a large amount of data that can notably be reused for clinical audits and scientific research. The sharing of these data and tools is generally affected by the lack of system interoperability. To overcome these issues, Observational Health Data Sciences and Informatics (OHDSI) developed the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to standardize EHR data and promote large-scale observational and longitudinal research. Anesthesia data have not previously been mapped into the OMOP CDM.Objective: The primary objective was to transform anesthesia data into the OMOP CDM. The secondary objective was to provide vocabularies, queries, and dashboards that might promote the exploitation and sharing of anesthesia data through the CDM.Methods: Using our local anesthesia data warehouse, a group of 5 experts from 5 different medical centers identified local concepts related to anesthesia. The concepts were then matched with standard concepts in the OHDSI vocabularies. We performed structural mapping between the design of our local anesthesia data warehouse and the OMOP CDM tables and fields. To validate the implementation of anesthesia data into the OMOP CDM, we developed a set of queries and dashboards.Results: We identified 522 concepts related to anesthesia care. They were classified as demographics, units, measurements, operating room steps, drugs, periods of interest, and features. After semantic mapping, 353 (67.7%) of these anesthesia concepts were mapped to OHDSI concepts. Further, 169 (32.3%) concepts related to periods and features were added to the OHDSI vocabularies. Then, 8 OMOP CDM tables were implemented with anesthesia data and 2 new tables (EPISODE and FEATURE) were added to store secondarily computed data. We integrated data from 5,72,609 operations and provided the code for a set of 8 queries and 4 dashboards related to anesthesia care.Conclusions: Generic data concerning demographics, drugs, units, measurements, and operating room steps were already available in OHDSI vocabularies. However, most of the intraoperative concepts (the duration of specific steps, an episode of hypotension, etc) were not present in OHDSI vocabularies. The OMOP mapping provided here enables anesthesia data reuse. [ABSTRACT FROM AUTHOR]

A data warehouse (DW) is a vast repository of data that facilitates decision-making for businesses and companies. This concept dates back to the 1980s and it has been widely accepted. One of the key points for the success of the process of data warehousing lies in the definition of the warehouse model depending on data sources and analysis needs. Once the data warehouse is designed, the content and structure of the data sources, as well as the requirements analysis are required to evolve, therefore, an evolution of the model must take place (diagram and data). In this context, several approaches have been developed to design and implement data warehouses. Nevertheless, there is no standard process that deals with designing all of the data warehouse layers, also, there is no software that encompasses this type of problem. In general, the majority of these approaches focus on a particular aspect of data warehouse such as data storage, ETL process, OLAP, reporting, etc, and does not cover its entire lifecycle. A Model-Driven Architecture (MDA) is a standard approach, its aims to support all phases of software manufacturing by promoting the use of models and the transformations between them. Moreover, this approach aims to automate the process of software engineering, thereby decreasing the cost of software development and enhancing its productivity. In this study, we present a systematic review of various works on the data warehouse design methods. We compare and discuss these works according to the criteria that seem relevant for this issue. We present a new design approach for multidimensional schemas construction from relational models using MDA techniques, we also develop the resulting research perspectives. [ABSTRACT FROM AUTHOR]

In the virtual world, the authentication process is used to prove the identity for both of the server and the clients. Therefore, the authentication is an urgent issue for sharing data between users in the data warehouse systems. This paper presents the enhancement of Data Warehouse Signature (DWS) framework through proposing a novel authentication and group-key distribution model, based on the modular symmetric polynomials, to distribute a secure group-key between the manager and the chosen executors. To the best of our knowledge, we are the first to propose a group-key based on the symmetric polynomial and the modular arithmetic. This leads to increase the security level of the proposed model from t, where t is the symmetric polynomial degree, to ∑ i = 1 c c i × t , where c is the number of executors in the DWS system. In this paper, enhancing the DWS achieves the high performance using a parallel computing in our middleware. The proposed model has the lowest cost of computation, communication and complexity overheads among the existing security models. Additionally, security analysis shows that the proposed model achieves the key security, prevents insider and outsider attacks, and provides forward and backward secrecy. [ABSTRACT FROM AUTHOR]

Even though several systems for Air Quality (AQ) monitoring have been in existence for over a decade, a research model for Knowledge Management (KM) of AQ data has to be created in order to enhance the decision-making and organize the air quality data collected from the Internet of Things (IoT) consumer devices. This model should be made more performant by ensuring greater flexibility and interoperability between devices and emerging technologies. In this context, we propose an approach for representing Data WareHouse (DWH) schema based on an ontology that captures the multidimensional knowledge of tools, techniques, and technologies used for novel AQ systems. This enhances decision-making by coping with potential problems such as data sources heterogeneity and covering the various phases of the decision-making life cycle. [ABSTRACT FROM AUTHOR]

Technologic advances in information management have rapidly changed laboratory testing and the practice of veterinary medicine. Timely and strategic sampling, same-day assays, and 24-h access to laboratory results allow for rapid implementation of intervention and treatment protocols. Although agent detection and monitoring systems have progressed, and wider tracking of diseases across veterinary diagnostic laboratories exists, such as by the National Animal Health Laboratory Network (NAHLN), the distinction between detection of agent and manifestation of disease is critical to improved disease management. The implementation of a consistent, intuitive, and useful disease diagnosis coding system, specific for veterinary medicine and applicable to multiple animal species within and between veterinary diagnostic laboratories, is the first phase of disease data aggregation. Feedback loops for continuous improvement that could aggregate existing clinical and laboratory databases to improve the value and applications of diagnostic processes and clinical interventions, with interactive capabilities between clinicians and diagnosticians, and that differentiate disease causation from mere agent detection, remain incomplete. Creating an interface that allows aggregation of existing data from clinicians, including final diagnosis, interventions, or treatments applied, and measures of outcomes, is the second phase. Prototypes for stakeholder cooperation, collaboration, and beta testing of this vision are in development and becoming a reality. We focus here on how such a system is being developed and utilized at the Iowa State University Veterinary Diagnostic Laboratory to facilitate evidence-based medicine and utilize diagnostic coding for continuous improvement of animal health and welfare. [ABSTRACT FROM AUTHOR]

Copyright of Information -- Wissenschaft und Praxis is the property of De Gruyter 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.)

Globally, the coronavirus epidemic has now hit lives of millions and thousands of people around the world. The growing threat of this virus continues rising as new cases appear every day. Yet, affected countries by coronavirus are currently taking important measures to remedy it by using Artificial Intelligence (AI) and Big Data technologies. According to the World Health Organization (WHO), AI and Big Data have performed an important role in China's response to COVID-19, the genetic mutation name for coronavirus. Predicting an epidemic emergence, from the coronavirus appearance to a person's predisposition to develop it, is fundamental to combating it. In this battle, Big Data is on the front line. However, Big Data cannot provide all of the expected insights and derive value from manipulated data. This is why we propose a semantic approach to facilitate the use of these data. In this paper, we present a novel approach that combines between the Semantic Web Services (SWS) and the Big Data characteristics in order to extract a significant information from multiple data sources that can be exploitable for generating real-time statistics and reports. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of InformaticsTechnologies is the property of Institute of Informatics, Gazi University 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.)

Over the past decade, the concept of data warehousing has been widely accepted. The main reason for building data warehouses is to improve the quality of information in order to achieve specific business objectives such as competitive advantage or improved decision-making. However, there is no formal method for deriving a multidimensional schema from heterogeneous databases that is recognized as a standard by the OMG and the professionals of the field. Which is why, in this paper, we present a model-driven approach (MDA) for the design of data warehouses. To apply the MDA approach to the Data warehouse construction process, we describe a multidimensional meta-model and specify a set of transformations from a UML meta-model which is mapped to a multidimensional meta-model. The transformation rules are programmed by the Query View Transformation (QVT) language. A case study illustrates our approach. It demonstrates how it reinforces the components traceability and reusability and how it globally improves the modeler's efficiency. Furthermore, the use of the UML, as a technique to build data warehouses, is an important facilitator which prepares our further work to automate this approach. [ABSTRACT FROM AUTHOR]

Big data have analytical potential that was hard to realize with available technologies. After new storage paradigms intended for big data such as NoSQL databases emerged, traditional systems got pushed out of the focus. The current research is focused on their reconciliation on different levels or paradigm replacement. Similarly, the emergence of NoSQL databases has started to push traditional (relational) data warehouses out of the research and even practical focus. Data warehousing is known for the strict modelling process, capturing the essence of the business processes. For that reason, a mere integration to bridge the NoSQL gap is not enough. It is necessary to deal with this issue on a higher abstraction level during the modelling phase. NoSQL databases generally lack clear, unambiguous schema, making the comprehension of their contents difficult and their integration and analysis harder. This motivated involving semantic web technologies to enrich NoSQL database contents by additional meaning and context. This paper reviews the application of semantics in data integration and data warehousing and analyses its potential in integrating NoSQL data and traditional data warehouses with some focus on document stores. Also, it gives a proposal of the future pursuit directions for the big data warehouse modelling phases. [ABSTRACT FROM AUTHOR]

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]

"Three fit" problems in the Bid Process Information System (BPIS) are still an open issue. The BPIS must be: integrated, flexible and interoperable in order to deal with the urbanization approach. Hence, operational, organizational, decision-making, and coop-erative dimensions are four dimensions that have been identified to deal with such failures. Accordingly, we propose an approach that focuses on the decision-making dimension of the BPIS. In addition, it represents data warehouse schema based on an ontology in order to capture the multidimensional bid-knowledge. This improves the resulting of data warehouse schema. [ABSTRACT FROM AUTHOR]

In the age of big data, entreprises' information systems are ingested with data generated from social media which raises the need to integrate it in their business intelligence process for better decision making. However, these new data, streaming, voluminous, unstructured and variant, bring existing data warehousing systems and integration tools to their knees which motivated us to conduct this research work. In this paper, we propose a large scale system based on distributed storage and parallel processing to succeed social media data warehousing. In fact, we combine Storm and Hadoop for structured events extraction from social media data and their integration in the data warehouse. We take the advantage of real time analysis of streaming data offered by Storm and batch processing of large volumes of data of Hadoop which facilitated streaming social media data analysis task. For conceptual representation, we propose a customized multidimensional model in which we add an intermediate table to connect the social media data warehouse with the enterprise data warehouse. We implement it using Oracle 12c and we fed it with events extracted from 1000 000 tweets using Pentaho data integration tool. [ABSTRACT FROM AUTHOR]

Data tipically evolve according to specific processes, with the consequent possibility to identify a profile of evolution: the values it may assume, the frequencies at which it changes, the temporal variation in relation to other data, or other constraints that are directly connected to the reference domain. A violation of these conditions could be the signal of different menaces that threat the system, as well as: attempts of a tampering or a cyber attack, a failure in the system operation, a bug in the applications which manage the life cycle of data. To detect such violations is not straightforward as processes could be unknown or hard to extract. In this paper we propose an approach to detect data anomalies. We represent data user behaviours in terms of labelled transition systems and through the model checking techniques we demonstrate the proposed modeling can be exploited to successfully detect data anomalies. [ABSTRACT FROM AUTHOR]

In order to enhance the business process, the Bid Process Information System (BPIS) should be made more performant by ensuring greater flexibility and interoperability between devices. Moreover, the specication of this system has to deal with "three fit" problems. To this end, four dimensions have been identified in order to cope with potential failures: operational, organizational, decision-making, and cooperative dimensions. In this paper, we focus on the decision-making dimension of the BPIS and propose an approach for representing data warehouse schema based on an ontology that captures the multidimensional bid-knowledge. [ABSTRACT FROM AUTHOR]

In order to enhance the business process, the Bid Process Information System (BPIS) should be made more performant by ensuring greater flexibility and interoperability between devices. Moreover, the specication of this system has to deal with "three fit" problems. To this end, four dimensions have been identified in order to cope with potential failures: operational, organizational, decision-making, and cooperative dimensions. In this paper, we focus on the decision-making dimension of the BPIS and propose an approach for representing data warehouse schema based on an ontology that captures the multidimensional bid-knowledge. [ABSTRACT FROM AUTHOR]

Traditional data warehouses are unable to meet the growing needs of the modern enterprise to integrate and analyze a wide variety of data generated by social, mobile and sensor sources. What is remarkable is that many companies have changed their data storage in NoSQL databases. Given the important role played by this type of databases, it is necessary to study NoSQL databases as a data source for the modeling and the implementation of data warehouses. This paper proposes a method to design the data warehouse schema from schema free databases known as NoSQL databases. Our proposal starts with the extraction of schemes from document-oriented databases as an example of NoSQL database. This extraction is performed based on the MapReduce paradigm. Then, it defines the structure identification graph from the extracted schemes. This graph will help the designer to identify the multidimensional concepts for each schema extracted in order to design the global data warehouse schema. [ABSTRACT FROM AUTHOR]

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To facilitate the development of new varieties and conserve plant germplasm data for future needs, Thailand organizations have developed plant germplasm search systems that provide access and exchange plant genetic resource data like the international organizations. However, the development of plant germplasm search systems using traditional data warehousing creates a limitation in terms of structural flexibility and scalability, as well as search performance. Accordingly, this paper proposes a JSON and index search system for the plant germplasm database named JINDEX. JINDEX is a new approach for implementing a galaxy schema in NoSQL based a hybrid key-value/document data model. Plant germplasm data is stored in JSON files as a document data model, providing a flexible structure of genetic resources for various plants. The plant germplasm data and index files are organized as an on-disk tree structure, which can be accessed regarding a key-value data model to retrieve plant germplasm data efficiently. Experimental results showed that the JINDEX-based plant germplasm search system outperforms the previous plant germplasm search system implemented in a relational database in terms of query response time. Additionally, this JINDEX-based plant germplasm search system has been deployed since 2020, demonstrating that it can work practically. [ABSTRACT FROM AUTHOR]

This article is intended to be a very explicit guide to the Big Data approach. The material is organized in three parts: the importance of 'humanizing' Big Data, the strategic role of business analysts in extracting the context or 'storyline' they say, and a guide to innovative BI tools for extracting data from value and acquiring of competitive intelligence. Digital information is growing at a rapid pace and in such a large amount that the traditional storage systems used for storing and managing them are outdated. An analytical workflow is an important part of a company's intellectual property and increases its value as it is popularized, re-used and completed. Current technology offers progress in terms of the speed, scalability and financial savings that organizations need to work in a world where data is the basis for competitive advantage. Numerous organizations experiencing a dramatic increase in information volumes appeal to Apache Hadoop, an open-source data processing technology to address the need to store and manage a large amount of information. [ABSTRACT FROM AUTHOR]

Objective: To present a data model for clinical legal medicine and the software based on that data model for both practitioners and researchers. The main functionalities of the presented software are computer-assisted production of medical certificates and data capture, storage and retrieval.Methods: The data model and the software were jointly developed by the department of forensic medicine of the Jean Verdier Hospital (Bondy, France) and an bioinformatics laboratory (LIMICS, Paris universities 6-13) between November 2015 and May 2016. The data model was built based on four sources: i) a template used in our department for producing standardised medical certificates; ii) a random sample of medical certificates produced by the forensic department; iii) anterior consensus between four healthcare professionals (two forensic practitioners, a psychologist and a forensic psychiatrist) and iv) anatomical dictionaries. The trial version of the open source software was first designed for examination of physical assault survivors.Results: An UML-like data model dedicated to clinical legal practice was built. The data model describes the terminology for examinations of sexual assault survivors, physical assault survivors, individuals kept in police custody and undocumented migrants for age estimation. A trial version of a software relying on the data model was developed and tested by three physicians.Discussion: The software allows files archiving, standardised data collection, extraction and assistance for certificate generation. It can be used for research purpose, by data exchange and analysis. Despite some current limitations of use, it is a tool which can be shared and used by other departments of forensic medicine and other specialties, improving data management and exploitation. Full integration with external sources, analytics software and use of a semantic interoperability framework are planned for the next months. [ABSTRACT FROM AUTHOR]

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The problem with telecommunications companies today is that transactional data is more extensive than existing source tables. This makes business reporting less efficient and overwhelms query processing results in data warehouses so that they do not meet business requirements. The fast and complex evolution of the digital world must be scalable to the data warehouse process, so that the authors implement it in the data warehouse using massive parallel processing (MPP) with the Greenplum database, so that business users can get reports faster and more optimally. This case study explains how the MPP system implements and measures the performance of the Greenplum database by performing complex queries in the data warehouse with parallel processing. Therefore, this case study analyzes whether the use of MPP systems can measure the scalability of throughput and the response time in the data warehouse so that system performance in the Greenplum database remains stable for daily, weekly, and monthly operations. [ABSTRACT FROM AUTHOR]

Introduction: Clinical data warehouses are often oriented toward integration and exploration of coded data. However narrative reports are of crucial importance for translational research. This paper describes Dr. Warehouse®, an open source data warehouse oriented toward clinical narrative reports and designed to support clinicians' day-to-day use.Method: Dr. Warehouse relies on an original database model to focus on documents in addition to facts. Besides classical querying functionalities, the system provides an advanced search engine and Graphical User Interfaces adapted to the exploration of text. Dr. Warehouse is dedicated to translational research with cohort recruitment capabilities, high throughput phenotyping and patient centric views (including similarity metrics among patients). These features leverage Natural Language Processing based on the extraction of UMLS® concepts, as well as negation and family history detection.Results: A survey conducted after 6 months of use at the Necker Children's Hospital shows a high rate of satisfaction among the users (96.6%). During this period, 122 users performed 2837 queries, accessed 4,267 patients' records and included 36,632 patients in 131 cohorts. The source code is available at this github link https://github.com/imagine-bdd/DRWH. A demonstration based on PubMed abstracts is available at https://imagine-plateforme-bdd.fr/dwh_pubmed/. [ABSTRACT FROM AUTHOR]