Big Data Mining and Semantic Technologies: Challenges and Opportunities

Big data a term coined due to the explosion in the quantity and diversity of high frequency digital data which is having a potential for valuable insights has drawn the most attention in the area of research and development. Converting big data to actionable insights requires depth understanding of big data, its characteristics, challenges and current technological trends. A rise of big data is changing the existing data storage, management, processing and analytical mechanisms and leads to the new architecture/ecosystems to handle big data applications. This paper covers finding of our research study about big data characteristic, various types of analysis associated with it and basic big data types. First, we are presenting the big data study from data mining and analysis perspective and discuss the challenges and next, we present the result of research study on meaningful use of big data in the context of semantic technologies. Moreover, we discuss various case studies related to social media analysis and recent development trends to identify potential research directions for big data with semantic technologies. DOI: 10.17762/ijritcc2321-8169.150711

Technical Research Priorities for Big Data

To drive innovation and competitiveness, organisations need to foster the development and broad adoption of data technologies, value-adding use cases and sustainable business models. Enabling an effective data ecosystem requires overcoming several technical challenges associated with the cost and complexity of management, processing, analysis and utilisation of data. This chapter details a community-driven initiative to identify and characterise the key technical research priorities for research and development in data technologies. The chapter examines the systemic and structured methodology used to gather inputs from over 200 stakeholder organisations. The result of the process identified five key technical research priorities in the areas of data management, data processing, data analytics, data visualisation and user interactions, and data protection, together with 28 sub-level challenges. The process also highlighted the important role of data standardisation, data engineering and DevOps for Big Data

CHAMALEON: Framework to improve Data Wrangling with Complex Data

Data transformation and schema conciliation are relevant topics in Industry due to the incorporation of data-intensive business processes in organizations. As the amount of data sources increases, the complexity of such data increases as well, leading to complex and nested data schemata. Nowadays, novel approaches are being employed in academia and Industry to assist non-expert users in transforming, integrating, and improving the quality of datasets (i.e., data wrangling). However, there is a lack of support for transforming semi-structured complex data. This article makes an state-of-the-art by identifying and analyzing the most relevant solutions that can be found in academia and Industry to transform this type of data. In addition, we propose a Domain-Specific Language (DSL) to support the transformation of complex data as a first approach to enhance data wrangling processes. We also develop a framework to implement the DSL and evaluate it in a real-world case study

Compare and Contrast ERP: PeopleSoft vs Workday

Shared by the same founder David Duffield, two major enterprise resource planning (ERP) software systems, PeopleSoft and Workday are competing for keeping and increasing their share in the global market. Focusing on PeopleSoft and Workday, this research explores how they are both similar and different in regard to their functions, architecture, deployment and updates, new features, customization, user interface, training and reporting. Additionally, following the current trend of customers switching from PeopleSoft to Workday, some pros and cons of that have been identified and illustrated based on use cases. Finally, an outlook into the future is offered based on the author’s insight

Bisnesmetriikan kerääminen ja visualisointi pilvipohjaisessa kehitysympäristössä

Monitoring cloud computing resources is a straightforward and common task for any cloud application developer. The problem with current monitoring solutions is that they only focus on infrastructure resources. Many companies on the other hand would need data about the business side of their applications. This thesis extends the current monitoring solutions to capture business metrics from within applications. The metrics are then visualized to quickly allow for better analysis of the data. The tool is composed of three main components. The metrics are captured with a Node.js library that is imported in the monitored application. The library sends the captured data to InfluxDB timeseries database. The data is visualized with Grafana which implements tables, graphs, and gauges. The provided command-line tool creates a file that can be imported in Grafana to create a new dashboard with graphs in it. The requirements for the tool were created through the needs of software developers and clients of web- and mobile-developer Codemate. An architectural design was made based on the requirements and then implemented on the AWS cloud platform on top of Kubernetes. The implementation was evaluated by testing it in a real production server. The tool is functional and it works as intended. The results from the evaluation prove that the tool created in this thesis can help companies gain better information about their products. Future work includes adding the metrics capture for other languages such as Go and Ruby as well as integrating the tool to Codemate’s new development environment. Further research can be done especially in improving performance of the solution in large systems.Pilviresurssien monitorointi on selkeä ja yleinen tehtävä jokaiselle pilvipalvelun kehittäjälle. Monitorointisovellukset keskittyvät vain infrastruktuuriresursseihin, vaikka monet nykyajan yritykset tarvitsisivat tarkempaa tietoa sovellusten bisnespuolesta. Tämä diplomityö laajentaa nykyisiä monitorointisovelluksia kattamaan bisnesmetriikan keräämisen applikaatioiden sisältä sekä visualisoi datan paremman analyysin mahdollistamiseksi. Diplomityössä kehitetty työkalu koostuu kolmesta osasta. Metriikat kerätään sovelluksista Node.js-kirjaston avulla, joka lisätään sovelluksen koodiin. Kirjasto lähettää dataa InfluxDB-tietokantaan, josta se visualisoidaan Grafanalla interaktiivisten kuvaajien sekä taulukoiden avulla. Grafanaan voidaan lisäksi luoda työpöytiä diplomityötä varten luodulla ohjelmalla. Bisnesmetriikan keräämiseen ja visualisointiin luotu työkalu määriteltiin ohjelmistokehittäjä Codematen ohjelmistoinsinöörien sekä asiakkaiden tarpeiden mukaan. Määrittelyä käytettiin työkalun arkkitehtuurin luomiseen, joka ohjasi käytännön toteutusta. Työkalu rakennettiin Amazonin AWS-palveluun Kuberneteksen päälle. Toteutetun työkalun toimivuus testattiin lopuksi asiakasympäristössä tuotantopalvelimella. Työkalun todettiin toimivan tarkoituksenmukaisesti. Testauksesta saadut tulokset osoittavat, että työkalu voi auttaa yrityksiä saamaan parempaa informaatiota ohjelmistotuotteistaan sekä niiden käytöstä. Työkalun kehitystä voidaan jatkaa laajentamalla sen toimintaa Go- ja Ruby-kielille sekä integroimalla se tiiviimmin Codematen uuteen kehitysympäristöön. Lisätutkimus erityisesti suorituskyvyn parantamiseen laajoissa järjestelmissä on tarpeen

Industrial Applications: New Solutions for the New Era

This book reprints articles from the Special Issue "Industrial Applications: New Solutions for the New Age" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of twelve published articles. This special edition belongs to the "Mechatronic and Intelligent Machines" section

ICSEA 2021: the sixteenth international conference on software engineering advances

The Sixteenth International Conference on Software Engineering Advances (ICSEA 2021), held on October 3 - 7, 2021 in Barcelona, Spain, continued a series of events covering a broad spectrum of software-related topics. The conference covered fundamentals on designing, implementing, testing, validating and maintaining various kinds of software. The tracks treated the topics from theory to practice, in terms of methodologies, design, implementation, testing, use cases, tools, and lessons learnt. The conference topics covered classical and advanced methodologies, open source, agile software, as well as software deployment and software economics and education. The conference had the following tracks: Advances in fundamentals for software development Advanced mechanisms for software development Advanced design tools for developing software Software engineering for service computing (SOA and Cloud) Advanced facilities for accessing software Software performance Software security, privacy, safeness Advances in software testing Specialized software advanced applications Web Accessibility Open source software Agile and Lean approaches in software engineering Software deployment and maintenance Software engineering techniques, metrics, and formalisms Software economics, adoption, and education Business technology Improving productivity in research on software engineering Trends and achievements Similar to the previous edition, this event continued to be very competitive in its selection process and very well perceived by the international software engineering community. As such, it is attracting excellent contributions and active participation from all over the world. We were very pleased to receive a large amount of top quality contributions. We take here the opportunity to warmly thank all the members of the ICSEA 2021 technical program committee as well as the numerous reviewers. The creation of such a broad and high quality conference program would not have been possible without their involvement. We also kindly thank all the authors that dedicated much of their time and efforts to contribute to the ICSEA 2021. We truly believe that thanks to all these efforts, the final conference program consists of top quality contributions. This event could also not have been a reality without the support of many individuals, organizations and sponsors. We also gratefully thank the members of the ICSEA 2021 organizing committee for their help in handling the logistics and for their work that is making this professional meeting a success. We hope the ICSEA 2021 was a successful international forum for the exchange of ideas and results between academia and industry and to promote further progress in software engineering research

Full Metadata Object profiling for flexible geoprocessing workflows

The design and running of complex geoprocessing workflows is an increasingly common geospatial modelling and analysis task. The Business Process Model and Notation (BPMN) standard, which provides a graphical representation of a workflow, allows stakeholders to discuss the scientific conceptual approach behind this modelling while also defining a machine-readable encoding in XML. Previous research has enabled the orchestration of Open Geospatial Consortium (OGC) Web Processing Services (WPS) with a BPMN workflow engine. However, the need for direct access to pre-defined data inputs and outputs results in a lack of flexibility during composition of the workflow and of efficiency during execution. This article develops metadata profiling approaches, described as two possible configurations, which enable workflow management at the meta-level through a coupling with a metadata catalogue. Specifically, a WPS profile and a BPMN profile are developed and tested using open-source components to achieve this coupling. A case study in the context of an event mapping task applied within a big data framework and based on analysis of the Global Database of Event Language and Tone (GDELT) database illustrates the two different architectures