3,525 research outputs found
Big data analytics:Computational intelligence techniques and application areas
Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment
Transitioning Applications to Semantic Web Services: An Automated Formal Approach
Semantic Web Services have been recognized as a promising technology that exhibits huge commercial potential, and attract significant attention from both industry and the research community. Despite expectations being high, the industrial take-up of Semantic Web Service technologies has been slower than expected. One of the main reasons is that many systems have been developed without considering the potential of the web in integrating services and sharing resources. Without a systematic methodology and proper tool support, the migration from legacy systems to Semantic Web Service-based systems can be a very tedious and expensive process, which carries a definite risk of failure. There is an urgent need to provide strategies which allow the migration of legacy systems to Semantic Web Services platforms, and also tools to support such a strategy. In this paper we propose a methodology for transitioning these applications to Semantic Web Services by taking the advantage of rigorous mathematical methods. Our methodology allows users to migrate their applications to Semantic Web Services platform automatically or semi-automatically
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Computerization of workflows, guidelines and care pathways: a review of implementation challenges for process-oriented health information systems
There is a need to integrate the various theoretical frameworks and formalisms for modeling clinical guidelines, workflows, and pathways, in order to move beyond providing support for individual clinical decisions and toward the provision of process-oriented, patient-centered, health information systems (HIS). In this review, we analyze the challenges in developing process-oriented HIS that formally model guidelines, workflows, and care pathways. A qualitative meta-synthesis was performed on studies published in English between 1995 and 2010 that addressed the modeling process and reported the exposition of a new methodology, model, system implementation, or system architecture. Thematic analysis, principal component analysis (PCA) and data visualisation techniques were used to identify and cluster the underlying implementation āchallengeā themes. One hundred and eight relevant studies were selected for review. Twenty-five underlying āchallengeā themes were identified. These were clustered into 10 distinct groups, from which a conceptual model of the implementation process was developed. We found that the development of systems supporting individual clinical decisions is evolving toward the implementation of adaptable care pathways on the semantic web, incorporating formal, clinical, and organizational ontologies, and the use of workflow management systems. These architectures now need to be implemented and evaluated on a wider scale within clinical settings
Considerations for a design and operations knowledge support system for Space Station Freedom
Engineering and operations of modern engineered systems depend critically upon detailed design and operations knowledge that is accurate and authoritative. A design and operations knowledge support system (DOKSS) is a modern computer-based information system providing knowledge about the creation, evolution, and growth of an engineered system. The purpose of a DOKSS is to provide convenient and effective access to this multifaceted information. The complexity of Space Station Freedom's (SSF's) systems, elements, interfaces, and organizations makes convenient access to design knowledge especially important, when compared to simpler systems. The life cycle length, being 30 or more years, adds a new dimension to space operations, maintenance, and evolution. Provided here is a review and discussion of design knowledge support systems to be delivered and operated as a critical part of the engineered system. A concept of a DOKSS for Space Station Freedom (SSF) is presented. This is followed by a detailed discussion of a DOKSS for the Lyndon B. Johnson Space Center and Work Package-2 portions of SSF
System Design for a Data-driven and Explainable Customer Sentiment Monitor
The most important goal of customer services is to keep the customer
satisfied. However, service resources are always limited and must be
prioritized. Therefore, it is important to identify customers who potentially
become unsatisfied and might lead to escalations. Today this prioritization of
customers is often done manually. Data science on IoT data (esp. log data) for
machine health monitoring, as well as analytics on enterprise data for customer
relationship management (CRM) have mainly been researched and applied
independently. In this paper, we present a framework for a data-driven decision
support system which combines IoT and enterprise data to model customer
sentiment. Such decision support systems can help to prioritize customers and
service resources to effectively troubleshoot problems or even avoid them. The
framework is applied in a real-world case study with a major medical device
manufacturer. This includes a fully automated and interpretable machine
learning pipeline designed to meet the requirements defined with domain experts
and end users. The overall framework is currently deployed, learns and
evaluates predictive models from terabytes of IoT and enterprise data to
actively monitor the customer sentiment for a fleet of thousands of high-end
medical devices. Furthermore, we provide an anonymized industrial benchmark
dataset for the research community
Legal Regulation of Algorithms From the Perspective of Interpretability
The human life in the age of artificial intelligence has undergone tremendous changes. Algorithm technology is widely developed and applied as one of the core technologies of artificial intelligence. However, a series of problems such as algorithm discrimination, algorithm killing, and āinformation cocoonā caused by unexplainable algorithms represented by artificial neural networks needed to be solved urgently,which forms a risk society. The algorithmic order is gradually āoffsideā into a new social order, which challenges the existing legal order. Because the existing legal order upholds the neutral value of technology tools and does not pay attention to the legal regulations of technology itself, it cannot make ethical prejudgment of unexplainable algorithms to prevent and control social risks. With the deepening of the āintelligenceā of algorithm technology, social risk is expanding. The field of algorithm technology creates interpretable algorithms to respond to social risks. However, due to the lack of legal value and institutional design support, the technological advantages of interpretable algorithms to prevent and control algorithm black boxes, āOffside orderā and coping with a risky society cannot be confirmed and guided by law. Therefore, it is an effective way to solve the risks in the age of artificial intelligence by taking the technical critical theory of risk society as the value basis and taking the interpretability of algorithms as a necessary condition for algorithm regulation
Machine Learning Aided Static Malware Analysis: A Survey and Tutorial
Malware analysis and detection techniques have been evolving during the last
decade as a reflection to development of different malware techniques to evade
network-based and host-based security protections. The fast growth in variety
and number of malware species made it very difficult for forensics
investigators to provide an on time response. Therefore, Machine Learning (ML)
aided malware analysis became a necessity to automate different aspects of
static and dynamic malware investigation. We believe that machine learning
aided static analysis can be used as a methodological approach in technical
Cyber Threats Intelligence (CTI) rather than resource-consuming dynamic malware
analysis that has been thoroughly studied before. In this paper, we address
this research gap by conducting an in-depth survey of different machine
learning methods for classification of static characteristics of 32-bit
malicious Portable Executable (PE32) Windows files and develop taxonomy for
better understanding of these techniques. Afterwards, we offer a tutorial on
how different machine learning techniques can be utilized in extraction and
analysis of a variety of static characteristic of PE binaries and evaluate
accuracy and practical generalization of these techniques. Finally, the results
of experimental study of all the method using common data was given to
demonstrate the accuracy and complexity. This paper may serve as a stepping
stone for future researchers in cross-disciplinary field of machine learning
aided malware forensics.Comment: 37 Page
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