Pattern discovery: A progressive visual analytic design to support categorical data analysis

When using data-mining tools to analyze big data, users often need tools to support the understanding of individual data attributes and control the analysis progress. This requires the integration of data-mining algorithms with interactive tools to manipulate data and analytical process. This is where visual analytics can help. More than simple visualization of a dataset or some computation results, visual analytics provides users an environment to iteratively explore different inputs or parameters and see the corresponding results. In this research, we explore a design of progressive visual analytics to support the analysis of categorical data with a data-mining algorithm, Apriori. Our study focuses on executing data mining techniques step-by-step and showing intermediate result at every stage to facilitate sense-making. Our design, called Pattern Discovery Tool, targets for a medical dataset. Starting with visualization of data properties and immediate feedback of users’ inputs or adjustments, Pattern Discovery Tool could help users detect interesting patterns and factors effectively and efficiently. Afterward, further analyses such as statistical methods could be conducted to test those possible theories.     

利用问题求解理论来研究交互式复杂信息的可视分析行为

面对大数据的挑战,力图将人的推理能力和计算系统的数据处理能力相结合的交 互式可视分析研究变得愈发重要。然而目前仍缺乏有效的认知理论来指导面向复杂信息的可视 分析系统的设计,诸如意义构建等现有的理论框架通常着眼于分析行为的外在特征,未能对此 类行为的内在认知机理进行深入研究。因此提出将问题求解作为一种理论框架来解释交互可视 分析行为的基本认知活动,并建议从非良构问题的角度来描述可视分析过程中用户所面临的主 要挑战,还从问题表征及问题求解策略等角度分析了可视分析系统对分析行为的影响。本研究 在理论上,将认知心理学领域的问题求解理论引入到交互可视分析行为的研究中,该方法对设 计面向复杂信息分析的其他类型交互系统也有启示作用;在实践层面上,从问题求解的支持角 度探索了可视分析系统的设计和评估问题。     

Computational Collective Intelligence with Big Data: Challenges and Opportunities

Collective intelligence has been an important research topic in many AI communities. With The big data phenomenon, we have been facing on many research problems on how to integrate the big data with collective intelligence. This special issue has selected 9 high quality papers covering various research issues.     

Guest Editors' Introduction: Artificial Intelligence for Homeland Security

In the post-9/11 world, information technology is an indispensable part of making our nation safer. Critical national security missions in the context of various data and technical domain challenges could benefit from establishing an intelligence and security informatics research discipline. Just as biomedical informatics addresses information management issues in biological and medical applications, ISI would address such issues for intelligence and security applications.The knowledge discovery from databases methodology shows promise in addressing unique ISI challenges. KDD has already proved successful in other information-intensive, knowledge-critical domains including business, engineering, biology, and medicine.This article is part of a special issue on Homeland Security.     

Big Data analytics for sustainability: Insight through technological innovation

How do information systems and big data analytics help to enable a sustainable future? This question is investigated in nine papers in this special issue that examine the issue of big data analytics for sustainability from a variety of perspectives. Broadly, these papers can be considered in four main areas: health, online behavior and consumption, safety and the environment, and methods to improve understanding of sustainability issues. Recent advances in data-driven decision-making analytics research focusing on different aspects of sustainability are discussed in these papers, including air pollution management, online health consultation services, gamification of exercise and health, sustainable urban mobility, the sustainable use of resources in hospitals, the design of anticrime information support systems, the interdependence effects among mobile social apps, networks of sustainable development goals, and the spillover effect of sustainable consumption.     

Intelligent systems in biology: why the excitement?

Biology has rapidly become a data-rich, information-hungry science because of recent massive data generation technologies. Our biological colleagues are designing more clever and informative experiments because of recent advances in molecular science. These experiments and data hold the key to the deepest secrets of biology and medicine, but we cannot fully analyze this data due to the wealth and complexity of the information available. The result is a great need for intelligent systems in biology. There are many opportunities for intelligent systems to help produce knowledge in biology and medicine. Intelligent systems probably helped design the last drug your doctor prescribed, and they were probably involved in some aspect of the last medical care you received. Intelligent computational analysis of the human genome will drive medicine for at least the next half-century. Intelligent systems are working on gene expression data to help understand genetic regulation and ultimately the regulated control of all life processes including cancer, regeneration, and aging. Knowledge bases of metabolic pathways and other biological networks make inferences in systems biology that, for example, let a pharmaceutical program target a pathogen pathway that does not exist in humans, resulting in fewer side effects to patients. Modern intelligent analysis of biological sequences produces the most accurate picture of evolution ever achieved. Knowledge-based empirical approaches currently are the most successful method known for general protein structure prediction. Intelligent literature-access systems exploit a knowledge flow exceeding half a million biomedical articles per year. Machine learning systems exploit heterogenous online databases whose exponential growth mimics Moore's law.     

Information fusion as an integrative cross-cutting enabler to achieve robust,explainable, and trustworthy medical artificial intelligence

Medical artificial intelligence (AI) systems have been remarkably successful, even outperforming human performance at certain tasks. There is no doubt that AI is important to improve human health in many ways and will disrupt various medical workflows in the future. Using AI to solve problems in medicine beyond the lab, in routine environments, we need to do more than to just improve the performance of existing AI methods. Robust AI solutions must be able to cope with imprecision, missing and incorrect information, and explain both the result and the process of how it was obtained to a medical expert. Using conceptual knowledge as a guiding model of reality can help to develop more robust, explainable, and less biased machine learning models that can ideally learn from less data. Achieving these goals will require an orchestrated effort that combines three complementary Frontier Research Areas: (1) Complex Networks and their Inference, (2) Graph causal models and counterfactuals, and (3) Verification and Explainability methods. The goal of this paper is to describe these three areas from a unified view and to motivate how information fusion in a comprehensive and integrative manner can not only help bring these three areas together, but also have a transformative role by bridging the gap between research and practical applications in the context of future trustworthy medical AI. This makes it imperative to include ethical and legal aspects as a cross-cutting discipline, because all future solutions must not only be ethically responsible, but also legally compliant.     

Associative retrieval in spatial big data based on spreading activation with semantic ontology

The opportunities associated with big data have helped generate significant interest, and big data analytics has emerged as an important area of study for both practitioners and researchers. For example, traditional cause–effect analysis and conditional retrieval fall short in dealing with data that are so large and complex. Associative retrieval, on the other hand, has been identified as a potential technique for big data. In this paper, we integrate the spreading activation (SA) algorithm and the ontology model in order to promote the associative retrieval of big data. In our approach, constraints based on variant weights of semantic links are considered with the aim of improving the spreading-activation process and ensuring the accuracy of search results. Semantic inference rules are also introduced to the SA algorithm to find latent spreading path and help obtain results which are more relevant. Our theoretical and experimental analysis demonstrate the utility of this approach.     

大数据分析中的计算智能研究现状与展望

随着产业界和科学界数据量的爆炸式增长,大数据技术和应用吸引了众多的关注.如何分析大数据,充分挖掘大数据的潜在价值,成为需要深入探讨的科学问题.计算智能是科学研究和工程实践中解决复杂问题的有效手段,是人工智能和信息科学的重要研究方向,应用计算智能方法进行大数据分析具有巨大的潜力.对大数据分析中的计算智能方法进行综述,结合大数据的特征,讨论了大数据分析中计算智能研究存在的问题和进一步的研究方向,阐述了数据源共享问题,并建议利用以天文学为代表的数据密集型基础科研领域的数据开展大数据分析研究.     

Harnessing Artificial Intelligence to Deliver Real-Time Intelligence and Business Process Improvements

As demand for data scientists in audit/Governance, risk management and compliance (GRC), and industry in general, outpaces supply, data science in a box—packaged analytics powered by artificial intelligence (AI) and guided machine learning—can bridge the gap to bring analytics to every major enterprise. Packaged analytics harness the power of AI and machine learning technologies to help operations, finance executives, and GRC professionals do their jobs better; optimize business processes; and deliver actionable insights for better decision making. This article will explore real-world case studies of how companies have used packaged analytics to achieve process improvements, better oversight over financial spend, and significant return on investment. It is a guide to internal auditors and their GRC counterparts on what is available and suggests they can partner or use the products independently and significantly contribute to their companies.     

Business value of big data analytics: A systems-theoretic approach and empirical test

Although big data analytics have been widely considered a key driver of marketing and innovation processes, whether and how big data analytics create business value has not been fully understood and empirically validated at a large scale. Taking social media analytics as an example, this paper is among the first attempts to theoretically explain and empirically test the market performance impact of big data analytics. Drawing on the systems theory, we explain how and why social media analytics create super-additive value through the synergies in functional complementarity between social media diversity for gathering big data from diverse social media channels and big data analytics for analyzing the gathered big data. Furthermore, we deepen our theorizing by considering the difference between small and medium enterprises (SMEs) and large firms in the required integration effort that enables the synergies of social media diversity and big data analytics. In line with this theorizing, we empirically test the synergistic effect of social media diversity and big data analytics by using a recent large-scale survey data set from 18,816 firms in Italy. We find that social media diversity and big data analytics have a positive interaction effect on market performance, which is more salient for SMEs than for large firms.     

Cancer diagnosis using artificial intelligence: a review

Artificial intelligence (AI) is the usage of scientific techniques to simulate human intellectual skills and to tackle complex medical issues involving complicated genetic defects such as cancer. The rapid expansion of AI in the past era has paved the way to optimum judgment-making by superior intellect, where the human brain is constrained to manage large information in a limited period. Cancer is a complicated ailment along with several genomic variants. AI-centred systems carry enormous potential in detecting these genomic alterations and abnormal protein communications at a very initial phase. The contemporary biomedical study is also dedicated to bringing AI expertise to hospitals securely and ethically. AI-centred support to diagnosticians and doctors can be the big surge ahead for the forecast of illness threat, identification, diagnosis, and therapies. The applications related to AI and Machine Learning (ML) in the identification of cancer and its therapy possess the potential to provide therapeutic support for quicker planning of a novel therapy for each person. Through the utilization of AI- based methods, scientists can work together in real-time and distribute their expertise digitally to possibly cure billions. In this review, the focus was on the study of linking biology with AI and describe how AI-centred support could assist oncologists in accurate therapy. It is essential to identify new biomarkers that inject drug defiance and discover medicinal goals to improve medication methods. The advent of the “next-generation sequencing” (NGS) programs resolves these challenges and has transformed the prospect of “Precision Oncology” (PO). NGS delivers numerous medical functions which are vital for hazard prediction, initial diagnosis of infection, “Sequence” identification and “Medical Imaging” (MI), precise diagnosis, “biomarker” detection, and recognition of medicinal goals for innovation in medicine. NGS creates a huge repository that requires specific “bioinformatics” sources to examine the information that is pertinent and medically important. The malignancy diagnostics and analytical forecast are improved with NGS and MI that provide superior quality images via AI technology. Irrespective of the advancements in technology, AI faces a few problems and constraints, and the clinical application of NGS continues to be authenticated. Through the steady progress of invention and expertise, the prospects of AI and PO look promising. The purpose of this review was to assess, evaluate, classify, and tackle the present developments in cancer diagnosis utilizing AI methods for breast, lung, liver, skin cancer, and leukaemia. The research emphasizes in what way cancer identification, the treatment procedure is aided by utilizing AI with supervised, unsupervised, and deep learning (DL) methods. Numerous AI methods were assessed on benchmark datasets with respect to “accuracy”, “sensitivity”, “specificity”, and “false-positive” (FP) metrics. Lastly, challenges along with future work were discussed.

     

Recent advances in social multimedia big data mining and applications

In the past decade, social media contributes significantly to the arrival of the Big Data era. Big Data has not only provided new solutions for social media mining and applications, but brought about a paradigm shift to many fields of data analytics. This special issue solicits recent related attempts in the multimedia community. We believe that the enclosed papers in this special issue provide a unique opportunity for multidisciplinary works connecting both the social media and big data contexts to multimedia computing.     

Optimizing the energy efficiency of chiller systems in the semiconductor industry through big data analytics and an empirical study

Big data analytics is playing a more and more prominent role in the manufacturing industry as corporations attempt to utilize vast amounts of data to optimize the operation of plants and factories to gain a competitive advantage. Since the advent of Industry 4.0, also known as smart manufacturing, big data analytics, combined with expert domain knowledge, is facilitating ever-greater levels of speed and automaticity in manufacturing processes. The semiconductor industry is a fundamental driver of this transformation; moreover, due to the highly complex and energy-consuming nature of the semiconductor manufacturing process, semiconductor fabrication facilities (fabs) can also benefit greatly from incorporating big data analytics to improve production and energy efficiency. This paper developed a big data analytics framework, along with an empirical study conducted in collaboration with a semiconductor manufacturer in Taiwan, to optimize the energy efficiency of chiller systems in semiconductor fabs. Chiller systems are one of the most energy-consuming systems within a typical modern fab. The developed big data analytics framework allows production managers to ensure that chiller systems operate at an optimized level of energy efficiency under dynamically changing conditions, while fulfilling the chilling demands. Compared to the commonly-used heuristics previously employed at the fab to tune chiller system parameters, by the utilization of big data analytics, it is shown that fabs can achieve substantial energy savings, greater than 12%. The developed framework and the lessons learned from the empirical study are not only generalizable but also useful for practitioners who are interested in applying big data analytics to optimize the performance of other equipment systems in fabs.     

How big data analytics enables the alliance relationship stability of contract farming in the age of digital transformation

Notwithstanding the potential of big data analytics technology for alliance management, there is a lack of understanding of how such digital technology influences alliance relationship stability (ARS). Drawing on the information technology-enabled organizational capabilities (IT-enabled OCs) perspective, this study empirically verifies that big data analytics promotes ARS and risk management capability. Moreover, market risk management capability (MRM) enhances ARS, and data quality moderates the relationship between big data analytics usage (BDU) and MRM. This research reveals the impact mechanism of BDU on the ARS. Implications for management and future research are presented as well.     

大数据分析平台建设与应用综述

大数据分析平台是开展大数据处理与分析应用所必需的基础设施。文章基于课题组开展大数据分析平台建设的科研成果与实践经验,结合大型企业实施行业应用项目的切身感受,从大数据分析平台设计、主流热点技术、行业应用案例三个方面进行介绍。文章首先分析了大数据分析平台的主要功能和体系架构,然后介绍了大数据分析平台的关键技术,重点介绍了 Spark技术的体系架构及核心组件,最后介绍了大数据技术在大规模制造业、零售业和智能电网三个领域的应用案例。     

Vergleich von Kompetenzanforderungen an Business-Intelligence- und Big-Data-Spezialisten

While many studies on big data analytics describe the data deluge and potential applications for such analytics, the required skill set for dealing with big data has not yet been studied empirically. The difference between big data (BD) and traditional business intelligence (BI) is also heavily discussed among practitioners and scholars. We conduct a latent semantic analysis (LSA) on job advertisements harvested from the online employment platform monster.com to extract information about the knowledge and skill requirements for BD and BI professionals. By analyzing and interpreting the statistical results of the LSA, we develop a competency taxonomy for big data and business intelligence. Our major findings are that (1) business knowledge is as important as technical skills for working successfully on BI and BD initiatives; (2) BI competency is characterized by skills related to commercial products of large software vendors, whereas BD jobs ask for strong software development and statistical skills; (3) the demand for BI competencies is still far bigger than the demand for BD competencies; and (4) BD initiatives are currently much more human-capital-intensive than BI projects are. Our findings can guide individual professionals, organizations, and academic institutions in assessing and advancing their BD and BI competencies.     

The State of the Art in Visual Analysis Approaches for Ocean and Atmospheric Datasets

The analysis of ocean and atmospheric datasets offers a unique set of challenges to scientists working in different application areas. These challenges include dealing with extremely large volumes of multidimensional data, supporting interactive visual analysis, ensembles exploration and visualization, exploring model sensitivities to inputs, mesoscale ocean features analysis, predictive analytics, heterogeneity and complexity of observational data, representing uncertainty, and many more. Researchers across disciplines collaborate to address such challenges, which led to significant research and development advances in ocean and atmospheric sciences, and also in several relevant areas such as visualization and visual analytics, big data analytics, machine learning and statistics. In this report, we perform an extensive survey of research advances in the visual analysis of ocean and atmospheric datasets. First, we survey the task requirements by conducting interviews with researchers, domain experts, and end users working with these datasets on a spectrum of analytics problems in the domain of ocean and atmospheric sciences. We then discuss existing models and frameworks related to data analysis, sense‐making, and knowledge discovery for visual analytics applications. We categorize the techniques, systems, and tools presented in the literature based on the taxonomies of task requirements, interaction methods, visualization techniques, machine learning and statistical methods, evaluation methods, data types, data dimensions and size, spatial scale and application areas. We then evaluate the task requirements identified based on our interviews with domain experts in the context of categorized research based on our taxonomies, and existing models and frameworks of visual analytics to determine the extent to which they fulfill these task requirements, and identify the gaps in current research. In the last part of this report, we summarize the trends, challenges, and opportunities for future research in this area. (see http://www.acm.org/about/class/class/2012 )     

Identifying biomarker candidates of influenza infection based on scalable time‐course big data of gene expression

Large‐scale biological data processing is an important topic in computational biology, which gives the promotion of many biomedical research. For example, a huge volume of gene expression data can help us achieve potentially useful medical knowledge and identify disease biomarker candidates. Nevertheless, a great deal of attention has been paid to unscalable single‐time‐point expression data after disease symptoms appear (outbreak period) and there have been few investigations into scalable time‐course expression data before disease symptoms appear (incubation) for each sample. By exploiting such dynamic big data in the incubation, we can easily catch early signals of disease states and prevent illness in the first place. In this study, we apply a new mathematical model on biological data of given incubations and identify biomarker candidates using an intellectualized method. The model narrows a large number of alternative genes into a few ones (top genes), which facilitate the discovery of genes related to disease. The aim of our work is to propose a powerful biomarker‐detecting tool, which helps people to aid early diagnosis or identify effective drug targets before the appearance of clinical symptoms.     

Toward practical guideline for design of image compression algorithms for biomedical applications

Improvements in medicine and healthcare are accelerating. Information generation, sharing, and expert analysis, play a great role in improving medical sciences. Big data produced by medical procedures in hospitals, laboratories, and research centers needs storage and transmission. Data compression is a critical tool that reduces the burden of storage and transmission. Medical images, in particular, require special consideration in terms of storage and transmissions. Unlike many other types of big data, medical images require lossless storage. Special purpose compression algorithms and codecs could compress variety of such images with superior performance compared to the general purpose lossless algorithms. For the medical images, many lossless algorithms have been proposed so far. A compression algorithm comprises of different stages. Before designing a special purpose compression method we need to know how much each stage contributes to the overall compression performance so we could accordingly invest time and effort in designing different stages. In order to compare and evaluate these multi-stage compression techniques and to design more efficient compression methods for big data applications, in this paper the effectiveness of each of these compression stages on the total performance of the algorithm is analyzed.