Awarding federal contracts is perceived as an excessively lengthy process. The purpose of this research is threefold: (1) to understand the drivers of procurement administrative lead time (PALT), (2) to identify opportunities to reduce PALT, and (3) to predict when specific requirements are likely to be awarded. These analyses will be performed using newly available, government-wide data for over 5 million federal contracts. ; Prepared for the Naval Postgraduate School, Monterey, CA 93943. ; Naval Postgraduate School ; Approved for public release; distribution is unlimited. ; Approved for public release; distribution is unlimited.
AbstractThe introduction of Big Data sets in the healthcare domain has presented opportunities to engage in analytics of very large sets containing both structured and unstructured data. With advances in information technology (IT), these data sets have become available from diverse sources at greatly increased rates. The availability of Big Data sets has introduced complexities that we must address, not only in terms of semantics and analytics but also in terms of data management, storage, and distribution. Currently, the capabilities to ingest, analyze, and manage multipetabyte data sets have underscored the limitations of our analytics capabilities supported by relational database management systems. This essay argues that an ontology‐based approach to data analytics provides a practical framework to address the semantic challenges presented by Big Data sets. No ontological framework can address the operational and management requirements introduced by the availability of Big Data sets, however. There are also a number of IT architectural factors that must be considered in implementing such a framework.
Big data analytics uses algorithms for decision-making and targeting of customers. These algorithms process large-scale data sets and create efficiencies in the decision-making process for organizations but are often incomprehensible to customers and inherently opaque in nature. Recent European Union regulations require that organizations communicate meaningful information to customers on the use of algorithms and the reasons behind decisions made about them. In this paper, we explore the use of explanations in big data analytics services. We rely on discourse ethics to argue that explanations can facilitate a balanced communication between organizations and customers, leading to transparency and trust for customers as well as customer engagement and reduced reputation risks for organizations. We conclude the paper by proposing future empirical research directions.
1. Data Analytics and Litigation -- 2. History of Data Analysis in US Courts -- 3. Examples of Litigation Involving Big Data Analytics -- 4. The Courts as Gatekeeper of Big Data Evidence -- 5. Indirect Use of Big Data Analytics in US Courts -- 6. Future Challenges and Recommendations
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Big data analytics (BDA) in healthcare has made a positive difference in the integration of Artificial Intelligence (AI) in advancements of analytical capabilities, while lowering the costs of medical care. The aim of this study is to improve the existing healthcare eSystem by implementing a Big Data Analytics (BDA) platform and to meet the requirements of the Czech Republic National Health Service (Tender-Id. VZ0036628, No. Z2017-035520). In addition to providing analytical capabilities on Linux platforms supporting current and near-future AI with machine-learning and data-mining algorithms, there is the need for ethical considerations mandating new ways to preserve privacy, all of which are preconditioned by the growing body of regulations and expectations. The presented BDA platform, has met all requirements (N > 100), including the healthcare industry-standard Transaction Processing Performance Council (TPC-H) decision support benchmark in compliance with the European Union (EU) and the Czech Republic legislations. Currently, the presented Proof of Concept (PoC) that has been upgraded to a production environment has unified isolated parts of Czech Republic healthcare over the past seven months. The reported PoC BDA platform, artefacts, and concepts are transferrable to healthcare systems in other countries interested in developing or upgrading their own national healthcare infrastructure in a cost-effective, secure, scalable and high-performance manner.
Big data analytics (BDA) in healthcare has made a positive difference in the integration of Artificial Intelligence (AI) in advancements of analytical capabilities, while lowering the costs of medical care. The aim of this study is to improve the existing healthcare eSystem by implementing a Big Data Analytics (BDA) platform and to meet the requirements of the Czech Republic National Health Service (Tender-Id. VZ0036628, No. Z2017-035520). In addition to providing analytical capabilities on Linux platforms supporting current and near-future AI with machine-learning and data-mining algorithms, there is the need for ethical considerations mandating new ways to preserve privacy, all of which are preconditioned by the growing body of regulations and expectations. The presented BDA platform, has met all requirements (N > ; 100), including the healthcare industry-standard Transaction Processing Performance Council (TPC-H) decision support benchmark in compliance with the European Union (EU) and the Czech Republic legislations. Currently, the presented Proof of Concept (PoC) that has been upgraded to a production environment has unified isolated parts of Czech Republic healthcare over the past seven months. The reported PoC BDA platform, artefacts, and concepts are transferrable to healthcare systems in other countries interested in developing or upgrading their own national healthcare infrastructure in a cost-effective, secure, scalable and high-performance manner.
In: International journal of cyber warfare and terrorism: IJCWT ; an official publication of the Information Resources Management Association, Band 6, Heft 3, S. 53-64
The Governments, military forces and other organisations responsible for cybersecurity deal with vast amounts of data that has to be understood in order to lead to intelligent decision making. Due to the vast amounts of information pertinent to cybersecurity, automation is required for processing and decision making, specifically to present advance warning of possible threats. The ability to detect patterns in vast data sets, and being able to understanding the significance of detected patterns are essential in the cyber defence domain. Big data technologies supported by semantic technologies can improve cybersecurity, and thus cyber defence by providing support for the processing and understanding of the huge amounts of information in the cyber environment. The term big data analytics refers to advanced analytic techniques such as machine learning, predictive analysis, and other intelligent processing techniques applied to large data sets that contain different data types. The purpose is to detect patterns, correlations, trends and other useful information. Semantic technologies is a knowledge representation paradigm where the meaning of data is encoded separately from the data itself. The use of semantic technologies such as logic-based systems to support decision making is becoming increasingly popular. However, most automated systems are currently based on syntactic rules. These rules are generally not sophisticated enough to deal with the complexity of decisions required to be made. The incorporation of semantic information allows for increased understanding and sophistication in cyber defence systems. This paper argues that both big data analytics and semantic technologies are necessary to provide counter measures against cyber threats. An overview of the use of semantic technologies and big data technologies in cyber defence is provided, and important areas for future research in the combined domains are discussed.
Purpose Big data analytics (BDA) gets all the attention these days, but as important—and perhaps even more important—is big data analytics quality (BDAQ). Although many companies realize a full return from BDA, others clearly struggle. It appears that quality dynamics and their holistic impact on firm performance are unresolved in data economy. The purpose of this paper is to draw on the resource-based view and information systems quality to develop a BDAQ model and measure its impact on firm performance.
Design/methodology/approach The study uses an online survey to collect data from 150 panel members in France from a leading market research firm. The participants in the study were business analysts and IT managers with analytics experience.
Findings The study confirms that perceived technology, talent and information quality are significant determinants of BDAQ. It also identifies that alignment between analytics quality and firm strategy moderates the relationship between BDAQ and firm performance.
Practical implications The findings inform practitioners that BDAQ is a hierarchical, multi-dimensional and context-specific model.
Originality/value The study advances theoretical understanding of the relationship between BDAQ and firm performance under the influence of firm strategy alignment.
