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In this paper, I posit that from a research point of view, Data Science is a language. More precisely Data Science is doing Science using computer science as a language for datafied sciences; much as mathematics is the language of, e.g., physics. From this viewpoint, three (classes) of challenges for computer science are identified; complementing the challenges the closely related Big Data problem already poses to computer science. I discuss the challenges with references to, in my opinion, related, interesting directions in computer science research; note, I claim neither that these directions are the most appropriate to solve the challenges nor that the cited references represent the best work in their field, they are inspirational to me. So, what are these challenges? Firstly, if computer science is to be a language, what should that language look like? While our traditional specifications such as pseudocode are an excellent way to convey what has been done, they fail for more mathematics like reasoning about computations. Secondly, if computer science is to function as a foundation of other, datafied, sciences, its own foundations should be in order. While we have excellent foundations for supervised learning-e.g., by having loss functions to optimize and, more general, by PAC learning (Valiant in Commun ACM 27(11):1134-1142, 1984)-this is far less true for unsupervised learning. Kolmogorov complexity-or, more general, Algorithmic Information Theory-provides a solid base (Li and Vitanyi in An introduction to Kolmogorov complexity and its applications, Springer, Berlin, 1993). It provides an objective criterion to choose between competing hypotheses, but it lacks, e.g., an objective measure of the uncertainty of a discovery that datafied sciences need. Thirdly, datafied sciences come with new conceptual challenges. Data-driven scientists come up with data analysis questions that sometimes do and sometimes don't, fit our conceptual toolkit. Clearly, computer science does not suffer from a lack of interesting, deep, research problems. However, the challenges posed by data science point to a large reservoir of untapped problems. Interesting, stimulating problems, not in the least because they are posed by our colleagues in datafied sciences. It is an exciting time to be a computer scientist.

2nd International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2020 -- 26 June 2020 through 27 June 2020 -- -- 162106 2-s2.0-85089705154 Recent progress in software and hardware has allowed different data measurements in a variety of fields to be captured. These measures are produced continuously at very fluctuating data rates. For example, network sensors, web logs and computer network traffic. Computer-intensive activities are the collection, query and removal of these data sets. Mining data sources include the recovery of knowledge systems incorporated into templates and trends on streams of non-stop information. Because of the importance of its applications and the increasing generation of data stream research, information. Information. Information. Analysis applications for data streams can range from critical scientific and astronomical applications to major business and financial applications. Algorithms, processes and structures Streaming challenges have been developed over the last three years. We present the newest in this increasingly important field in this review paper. © 2020 IEEE.

Objective This article reviews the available evidence and guidance on methods to identify reports of quasi-experimental (QE) studies to inform systematic reviews of health care, public health, international development, education, crime and justice, and social welfare. Study Design and Setting Research, guidance, and examples of search strategies were identified by searching a range of databases, key guidance documents, selected reviews, conference proceedings, and personal communication. Current practice and research evidence were summarized. Results Four thousand nine hundred twenty-four records were retrieved by database searches, and additional documents were obtained by other searches. QE studies are challenging to identify efficiently because they have no standardized nomenclature and may be indexed in various ways. Reliable search filters are not available. There is a lack of specific resources devoted to collecting QE studies and little evidence on where best to search. Conclusion Searches to identify QE studies should search a range of resources and, until indexing improves, use strategies that focus on the topic rather than the study design. Better definitions, better indexing in databases, prospective registers, and reporting guidance are required to improve the retrieval of QE studies and promote systematic reviews of what works based on the evidence from such studies.

Emergency situations associated with falls are a serious concern for an aging society. Yet following the recent development within ICT, a significant number of solutions have been proposed to track body movement and detect falls using various sensor technologies, thereby facilitating fall detection and in some cases prevention. A number of recent reviews on fall detection methods using ICT technologies have emerged in the literature and an increasingly popular approach considers combining information from several sensor sources to assess falls. The aim of this paper is to review in detail the subfield of fall detection techniques that explicitly considers the use of multisensor fusion based methods to assess and determine falls. The paper highlights key differences between the single sensor-based approach and a multifusion one. The paper also describes and categorizes the various systems used, provides information on the challenges of a multifusion approach, and finally discusses trends for future work.

To survive worldwide competitions of research and development in the current rapid increase of information, decision-makers and researchers need to be supported to find promising research fields and papers. But finding those fields from an available data in too much heavy flood of information becomes difficult. We aim to develop a methodology supporting to find emerging leading papers with a bibliometric approach. The analyses in this work are about four academic domains using our time transition analysis. In the time transition analysis, after citation networks are constructed, centralities of each paper are calculated and their changes are tracked. Then, the centralities are plotted, and the features of the leading papers are extracted. Based on the features, we proposed ways to detect the leading papers by focusing on in-degree centrality and its transition. This work will contribute to finding the leading paper, and it is useful for decision-makers and researchers to decide the worthy research topic to invest their resources.

Scientific breakthroughs are rare events, and usually recognized retrospectively. We developed methods for early detection of candidate breakthroughs, based on dynamics of publication citations and used a quantitative approach to identify typical citation patterns of known breakthrough papers and a larger group of highly cited papers. Based on these analyses, we proposed two forecasting models that were validated using statistical methods to derive confidence levels. These findings can be used to inform research portfolio management practices.

Recommendation algorithms have been researched extensively to help people deal with abundance of information. In recent years, the incorporation of multiple relevance criteria has attracted increased interest. Such multi-criteria recommendation approaches are researched as a paradigm for building intelligent systems that can be tailored to multiple interest indicators of end-users – such as combinations of implicit and explicit interest indicators in the form of ratings or ratings on multiple relevance dimensions. Nevertheless, evaluation of these recommendation techniques in the context of real-life applications still remains rather limited. Previous studies dealing with the evaluation of recommender systems have outlined that the performance of such algorithms is often dependent on the dataset – and indicate the importance of carrying out careful testing and parameterization. Especially when looking at large scale datasets, it becomes very difficult to deploy evaluation methods that may help in assessing the effect that different system components have to the overall design. In this paper, we study how layered evaluation can be applied for the case of a multi-criteria recommendation service that we plan to deploy for paper recommendation using the Mendeley dataset. The paper introduces layered evaluation and suggests two experiments that may help assess the components of the envisaged system separately. ispartof: pages:1189-1197 ispartof: Procedia Computer Science vol:18 pages:1189-1197 ispartof: 2013 International Conference on Computational Science location:SPAIN, Barcelona date:5 Jun - 7 Jun 2013 status: published

To improve the access to medical information is necessary to design and implement integrated informatics techniques aimed to gather data from different and heterogeneous sources. This paper describes the technologies used to integrate data coming from the electronic medical record of the IRCCS Fondazione Maugeri (FSM) hospital of Pavia, Italy, and combines them with administrative, pharmacy drugs purchase coming from the local healthcare agency (ASL) of the Pavia area and environmental open data of the same region. The integration process is focused on data coming from a cohort of one thousand patients diagnosed with Type 2 Diabetes Mellitus (T2DM). Data analysis and temporal data mining techniques have been integrated to enhance the initial dataset allowing the possibility to stratify patients using further information coming from the mined data like behavioral patterns of prescription-related drug purchases and other frequent clinical temporal patterns, through the use of an intuitive dashboard controlled system.

Traditional activity-centric process modeling languages treat data as simple black boxes acting as input or output for activities. Many alternate and emerging process modeling paradigms, such as case handling and artifact-centric process modeling, give data a more central role. This is achieved by introducing lifecycles and states for data objects, which is beneficial when modeling data-or knowledge-intensive processes. We assume that traditional activity-centric process modeling languages lack the capabilities to adequately capture the complexity of such processes. To verify this assumption we conducted an online interview among BPM experts. The results not only allow us to identify various profiles of persons modeling business processes, but also the problems that exist in contemporary modeling languages w.r.t. The modeling of business data. Overall, this preliminary empirical study confirms the necessity of data-awareness in process modeling notations in general.

Fungi are versatile organisms; they exist on earth in all extremes of conditions. Fungi are sources of important chemical entities which may be both beneficial and deleterious. Biotechnology has helped to harness this potential of Fungi in a positive direction. The advancements in Genomics and Proteomics have opened up new horizon in research. Improved advanced Molecular Biological Technologies have given a boost to our understanding of genes and helped us to exploit the full potential of Fungi. Bioinformatics and Statistical sciences are indispensable in this regard. Databases are available, providing fast, efficient, meaningful interpretation and analysis of vast amounts of data generated in scientific laboratories.

Whenever a research scholar starts working on some innovative ideas he/she searches for the domain specific technical research articles published as research papers in most of the international journals, conferences or workshops. The problems associated with these papers are similarity in contents and repeated relevant information. Reading these all relatedpapers completely one by one to get the latest research developments in the interested domain is time-consuming, unnecessary, irrelevant, cumbersome and impossible. These problems are solved by developing a innovative solution, which optimizes and summarizes these research papers as an aid for research scholars using various Data Mining strategies. This helps research scholar for getting short, condensed, accurate and most relevant summarized information i.e. overview of domain-specific topic-based contents from research papers. Data mining strategies such as extraction and clustering are used for identifying ‘Research Relevant Novel’ (RRN) terms. Extracting relevant sentences from multiple papers uses ‘Maximal Marginal Relevance’ (MMR) criteria containing RRN terms. In addition to optimized and summarized contents, this paperalso informs earlier and latest research developments, progress, and challenges and future scope in particular field of study through various research categories identified such as research methods/techniques/approaches used, comparisons with existing research and providing starting material for further innovation.

We summarize the JRS’2012 Data Mining Competition on “Topical Classification of Biomedical Research Papers”, held between January 2, 2012 and March 30, 2012 as an interactive on-line contest hosted on the TunedIT platform ( http://tunedit.org ). We present the scope and background of the challenge task, the evaluation procedure, the progress, and the results. We also present a scalable method for the contest data generation from biomedical research papers.

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Through massive parallelism, distributed systems enable the multiplication of productivity. Unfortunately, increas- ing the scale of available machines to users will also mul- tiply debugging when failure occurs. Data mining allows the extraction of patterns within large amounts of data and therefore forms the foundation for a useful method of de- bugging, particularly within such distributed systems. This paper outlines a successful application of data mining in troubleshooting distributed systems, proposes a framework for further study, and speculates on other future work. We propose that data mining techniques can be applied to the problem of large scale troubleshooting. If both jobs and the resources that they consume are annotated with structured information relevant to success or failure, then classification algorithms can be used to find properties of each that correlate with success or failure. In the one- million jobs example above, an ideal troubleshooter would report to the user something like: Your jobs always fail on Linux 2.8 machines, always fail on cluster X between mid- night and 6 A.M, and fail with 50% probability on machines owned by user Y. Further, these discoveries may be used to automatically avoid making bad placement decisions that waste time and resources. We hasten to note that this form of data mining is not a panacea. It does not explain why failures happen, or make any attempt to diagnose problems in fine detail. It only proposes to the user properties correlated with suc- cess or failure. Other tools and techniques may be applied to extract causes. Rather, data mining allows the user of a large system to rapidly make generalizations to improve the throughput and reliability of a system without engag- ing in low level debugging. These generalizations may be used later at leisure to locate and repair problems. In addi- tion, the problem of distributed debugging, with its unique idiosyncracies and dynamics, lends itself as a compelling application for data mining research. Standard off-the-shelf methodologies might not be directly applicable for large, dynamic, and evolving system. It is desired to implement techniques that are capable of incremental self-revision and adaptation. The goal of our paper is to serve as a proof-of- concept and identify venues for compelling future research.

Most commercial data mining products provide a large number of models and tools for performing various data mining tasks, but few provide intelligent assistance for addressing many important decisions that must be considered during the mining process. In this paper, we propose the realization of a hybrid data mining assistant, based on the CBR paradigm and the use of an ontology, in order to empower the user during the various phases of the data mining process.

Data mining as an applied research field is still causing great expectations among organizations which want to raise the utility they are getting from their huge databases and data warehouses. There exist too few success stories about organizations having managed to satisfy even some of those expectations. This situation is very similar to the one inside the information systems (IS) field, especially earlier but even currently. The recent lively debate about the identity of the IS discipline included also the analysis concerning the customers of IS research. Inspired by IS researchers' insights related to the topic, we ask the question "who is our customer?" as data mining researchers. With this we want to raise to discussion the border that limits the topics 'acceptable' to work with as a data mining researcher. We suggest in this paper that the border should be transferred more clearly towards the direction so that beside the technical concerns also at least some user- and organization-related research questions are included

Information or data fusion of data from different sources are ubiquitous in many applications, from epidemiology, medical, biological, political, and intelligence to military applications. Data fusion involves integration of spectral, imaging, text, and many other sensor data. For example, in epidemiology, information is often obtained based on many studies conducted by different researchers at different regions with different protocols. In the medical field, the diagnosis of a disease is often based on imaging (MRI, X-Ray, CT), clinical examination, and lab results. In the biological field, information is obtained based on studies conducted on many different species. In military field, information is obtained based on data from radar sensors, text messages, chemical biological sensor, acoustic sensor, optical warning and many other sources. Many methodologies are used in the data integration process, from classical, Bayesian, to evidence based expert systems. The implementation of the data integration ranges from pure software design to a mixture of software and hardware. In this review we summarize the methodologies and implementations of data fusion process, and illustrate in more detail the methodologies involved in three examples. We propose a unified multi-stage and multi-path mapping approach to the data fusion process, and point out future prospects andmore » challenges.« less

The government, corporate, and industrial communities are faced with an ever increasing number of databases. These databases need not only to be managed, but also explored. The first requires secure access to distributed heterogeneous multimedia databases with rich metadata and having to meet timing constraints. The second requires exploratory tools supporting the identification of domain and mission critical elements such as patterns in data access (e.g., security breach determinations), patterns in data (e.g., marketing and clustering), or for patterns in transactions (e.g., data compression), to site a few. Knowledge Discovery in Databases is a relatively new research area that employs a variety of tools to explore and identify structure and patterns in these large databases. Often the data is preprocessed to facilitate such computations (data warehousing). The data is then mined for specific rules that are built incrementally and often steered by users with a specific set of goals in mind.

Keeping up with the rapidly growing literature has become virtually impossible for most scientists. This can have dire consequences. First, we may waste research time and resources on reinventing the wheel simply because we can no longer maintain a reliable grasp on the published literature. Second, and perhaps more detrimental, judicious (or serendipitous) combination of knowledge from different scientific disciplines, which would require following disparate and distinct research literatures, is rapidly becoming impossible for even the most ardent readers of research publications. Text mining -- the automated extraction of information from (electronically) published sources -- could potentially fulfil an important role -- but only if we know how to harness its strengths and overcome its weaknesses. As we do not expect that the rate at which scientific results are published will decrease, text mining tools are now becoming essential in order to cope with, and derive maximum benefit from, this information explosion. In genomics, this is particularly pressing as more and more rare disease-causing variants are found and need to be understood. Not being conversant with this technology may put scientists and biomedical regulators at a severe disadvantage. In this review, we introduce the basic concepts underlying modern text mining and its applications in genomics and systems biology. We hope that this review will serve three purposes: (i) to provide a timely and useful overview of the current status of this field, including a survey of present challenges; (ii) to enable researchers to decide how and when to apply text mining tools in their own research; and (iii) to highlight how the research communities in genomics and systems biology can help to make text mining from biomedical abstracts and texts more straightforward.

After its discovery in the 1950 s, the autophagy research field has seen its annual number of publications climb from tens to thousands. The ever-growing number of autophagy publications is a wealth of information but presents a challenge to researchers, especially those new to the field, who are looking for a general overview of the field to, for example, determine current topics of the field or formulate new hypotheses. Here, we employed text mining tools to extract research trends in the autophagy field, including those of genes, terms, and topics. The publication trend of the field can be separated into three phases. The exponential rise in publication number began in the last phase and is most likely spurred by a series of highly cited research papers published in previous phases. The exponential increase in papers has resulted in a larger variety of research topics, with the majority involving those that are directly physiologically relevant, such as disease and modulating autophagy. Our findings provide researchers a summary of the history of the autophagy research field and perhaps hints of what is to come. Abbreviations: 5Y-IF: 5-year impact factor; AIS: article influence score; EM: electron microscopy; HGNC: HUGO gene nomenclature committee; LDA: latent Dirichlet allocation; MeSH: medical subject headings; ncRNA: non-coding RNA

OBJECTIVES: The International Society for Bipolar Disorders Big Data Task Force assembled leading researchers in the field of bipolar disorder (BD), machine learning, and big data with extensive experience to evaluate the rationale of machine learning and big data analytics strategies for BD.METHOD: A task force was convened to examine and integrate findings from the scientific literature related to machine learning and big data based studies to clarify terminology and to describe challenges and potential applications in the field of BD. We also systematically searched PubMed, Embase, and Web of Science for articles published up to January 2019 that used machine learning in BD.RESULTS: The results suggested that big data analytics has the potential to provide risk calculators to aid in treatment decisions and predict clinical prognosis, including suicidality, for individual patients. This approach can advance diagnosis by enabling discovery of more relevant data-driven phenotypes, as well as by predicting transition to the disorder in high-risk unaffected subjects. We also discuss the most frequent challenges that big data analytics applications can face, such as heterogeneity, lack of external validation and replication of some studies, cost and non-stationary distribution of the data, and lack of appropriate funding.CONCLUSION: Machine learning-based studies, including atheoretical data-driven big data approaches, provide an opportunity to more accurately detect those who are at risk, parse-relevant phenotypes as well as inform treatment selection and prognosis. However, several methodological challenges need to be addressed in order to translate research findings to clinical settings.

Background The Appalachian population is distinct, not just culturally and geographically but also in its health care needs, facing the most health care disparities in the United States. To meet these unique demands, Appalachian medical centers need an arsenal of analytics and data science tools with the foundation of a centralized data warehouse to transform health care data into actionable clinical interventions. However, this is an especially challenging task given the fragmented state of medical data within Appalachia and the need for integration of other types of data such as environmental, social, and economic with medical data. Objective This paper aims to present the structure and process of the development of an integrated platform at a midlevel Appalachian academic medical center along with its initial uses. Methods The Appalachian Informatics Platform was developed by the Appalachian Clinical and Translational Science Institute’s Division of Clinical Informatics and consists of 4 major components: a centralized clinical data warehouse, modeling (statistical and machine learning), visualization, and model evaluation. Data from different clinical systems, billing systems, and state- or national-level data sets were integrated into a centralized data warehouse. The platform supports research efforts by enabling curation and analysis of data using the different components, as appropriate. Results The Appalachian Informatics Platform is functional and has supported several research efforts since its implementation for a variety of purposes, such as increasing knowledge of the pathophysiology of diseases, risk identification, risk prediction, and health care resource utilization research and estimation of the economic impact of diseases. Conclusions The platform provides an inexpensive yet seamless way to translate clinical and translational research ideas into clinical applications for regions similar to Appalachia that have limited resources and a largely rural population.

Motivation Most currently available text mining tools share two characteristics that make them less than optimal for use by biomedical researchers: they require extensive specialist skills in natural language processing and they were built on the assumption that they should optimize global performance metrics on representative datasets. This is a problem because most end-users are not natural language processing specialists and because biomedical researchers often care less about global metrics like F-measure or representative datasets than they do about more granular metrics such as precision and recall on their own specialized datasets. Thus, there are fundamental mismatches between the assumptions of much text mining work and the preferences of potential end-users. Results This article introduces the concept of Agile text mining, and presents the PubAnnotation ecosystem as an example implementation. The system approaches the problems from two perspectives: it allows the reformulation of text mining by biomedical researchers from the task of assembling a complete system to the task of retrieving warehoused annotations, and it makes it possible to do very targeted customization of the pre-existing system to address specific end-user requirements. Two use cases are presented: assisted curation of the GlycoEpitope database, and assessing coverage in the literature of pre-eclampsia-associated genes. Availability and implementation The three tools that make up the ecosystem, PubAnnotation, PubDictionaries and TextAE are publicly available as web services, and also as open source projects. The dictionaries and the annotation datasets associated with the use cases are all publicly available through PubDictionaries and PubAnnotation, respectively.

Background Social media are considered promising and viable sources of data for gaining insights into various disease conditions and patients’ attitudes, behaviors, and medications. They can be used to recognize communication and behavioral themes of problematic use of prescription drugs. However, mining and analyzing social media data have challenges and limitations related to topic deduction and data quality. As a result, we need a structured approach to analyze social media content related to drug abuse in a manner that can mitigate the challenges and limitations surrounding the use of such data. Objective This study aimed to develop and evaluate a framework for mining and analyzing social media content related to drug abuse. The framework is designed to mitigate challenges and limitations related to topic deduction and data quality in social media data analytics for drug abuse. Methods The proposed framework started with defining different terms related to the keywords, categories, and characteristics of the topic of interest. We then used the Crimson Hexagon platform to collect data based on a search query informed by a drug abuse ontology developed using the identified terms. We subsequently preprocessed the data and examined the quality using an evaluation matrix. Finally, a suitable data analysis approach could be used to analyze the collected data. Results The framework was evaluated using the opioid epidemic as a drug abuse case analysis. We demonstrated the applicability of the proposed framework to identify public concerns toward the opioid epidemic and the most discussed topics on social media related to opioids. The results from the case analysis showed that the framework could improve the discovery and identification of topics in social media domains characterized by a plethora of highly diverse terms and lack of a commonly available dictionary or language by the community, such as in the case of opioid and drug abuse. Conclusions The proposed framework addressed the challenges related to topic detection and data quality. We demonstrated the applicability of the proposed framework to identify the common concerns toward the opioid epidemic and the most discussed topics on social media related to opioids.