The 1st International Conference on Datadriven Knowledge Discovery:When Data Science Meets Information Science.June 19–22,2016,Beijing·China

The 1st International Conference on Data-driven Knowledge Discovery:When Data Science Meets Information Science took place at the National Science Library(NSL),Chinese Academy of Sciences(CAS)in Beijing from June 19 till June 22,2016.The Conference was opened by NSL Director Xiangyang Huang,who

Predicate Oriented Pattern Analysis for Biomedical Knowledge Discovery

In the current biomedical data movement, numerous efforts have been made to convert and normalize a large number of traditional structured and unstructured data (e.g., EHRs, reports) to semi-structured data (e.g., RDF, OWL). With the increasing number of semi-structured data coming into the biomedical community, data integration and knowledge discovery from heterogeneous domains become important research problem. In the application level, detection of related concepts among medical ontologies is an important goal of life science research. It is more crucial to figure out how different concepts are related within a single ontology or across multiple ontologies by analysing predicates in different knowledge bases. However, the world today is one of information explosion, and it is extremely difficult for biomedical researchers to find existing or potential predicates to perform linking among cross domain concepts without any support from schema pattern analysis. Therefore, there is a need for a mechanism to do predicate oriented pattern analysis to partition heterogeneous ontologies into closer small topics and do query generation to discover cross domain knowledge from each topic. In this paper, we present such a model that predicates oriented pattern analysis based on their close relationship and generates a similarity matrix. Based on this similarity matrix, we apply an innovated unsupervised learning algorithm to partition large data sets into smaller and closer topics and generate meaningful queries to fully discover knowledge over a set of interlinked data sources. We have implemented a prototype system named BmQGen and evaluate the proposed model with colorectal surgical cohort from the Mayo Clinic.

CALL FOR PAPERS Workshop on Intelligence and Security Informatics (WISI’06) in conjunction with the Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD’06)

Important Dates Submission due November 15, 2005 Notification of acceptance December 30, 2005 Camera-ready copy due January 10, 2006 Workshop Scope Intelligence and Security Informatics (ISI) can be broadly defined as the study of the development and use of advanced information technologies and systems for national and international security-related applications. The First and Second Symposiums on ISI were held in Tucson,Arizona,in 2003 and 2004,respectively. In 2005,the IEEE International Conference on ISI was held in Atlanta,Georgia. These ISI conferences have brought together academic researchers,law enforcement and intelligence experts,information technology consultant and practitioners to discuss their research and practice related to various ISI topics including ISI data management,data and text mining for ISI applications,terrorism informatics,deception detection,terrorist and criminal social network analysis,crime analysis,monitoring and surveillance,policy studies and evaluation,information assurance,among others. We continue this stream of ISI conferences by organizing the Workshop on Intelligence and Security Informatics (WISI’06) in conjunction with the Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD’06). WISI’06 will provide a stimulating forum for ISI researchers in Pacific Asia and other regions of the world to exchange ideas and report research progress. The workshop also welcomes contributions dealing with ISI challenges specific to the Pacific Asian region.

Knowledge Discovery in Learning Management System Using Piecewise Linear Regression

Recent developments in database technology have seen a wide variety of data being stored in huge collections. The wide variety makes the analysis tasks of a generic database a strenuous task in knowledge discovery. One approach is to summarize large datasets in such a way that the resulting summary dataset is of manageable size. Histogram has received significant attention as summarization/representative object for large database. But, it suffers from computational and space complexity. In this paper, we propose an idea to transform the histogram object into a Piecewise Linear Regression (PLR) line object and suggest that PLR objects can be less computational and storage intensive while compared to those of histograms. On the other hand to carry out a cluster analysis, we propose a distance measure for computing the distance between the PLR lines. Case study is presented based on the real data of online education system LMS. This demonstrates that PLR is a powerful knowledge representative for very large database.

Smart Approaches to Efficient Text Mining for Categorizing Sexual Reproductive Health Short Messages into Key Themes

To promote behavioral change among adolescents in Zambia, the National HIV/AIDS/STI/TB Council, in collaboration with UNICEF, developed the Zambia U-Report platform. This platform provides young people with improved access to information on various Sexual Reproductive Health topics through Short Messaging Service (SMS) messages. Over the years, the platform has accumulated millions of incoming and outgoing messages, which need to be categorized into key thematic areas for better tracking of sexual reproductive health knowledge gaps among young people. The current manual categorization process of these text messages is inefficient and time-consuming and this study aims to automate the process for improved analysis using text-mining techniques. Firstly, the study investigates the current text message categorization process and identifies a list of categories adopted by counselors over time which are then used to build and train a categorization model. Secondly, the study presents a proof of concept tool that automates the categorization of U-report messages into key thematic areas using the developed categorization model. Finally, it compares the performance and effectiveness of the developed proof of concept tool against the manual system. The study used a dataset comprising 206,625 text messages. The current process would take roughly 2.82 years to categorise this dataset whereas the trained SVM model would require only 6.4 minutes while achieving an accuracy of 70.4% demonstrating that the automated method is significantly faster, more scalable, and consistent when compared to the current manual categorization. These advantages make the SVM model a more efficient and effective tool for categorizing large unstructured text datasets. These results and the proof-of-concept tool developed demonstrate the potential for enhancing the efficiency and accuracy of message categorization on the Zambia U-report platform and other similar text messages-based platforms.

Rediscovering Don Swanson:The Past,Present and Future of Literature-based Discovery

Purpose: The late Don R.Swanson was well appreciated during his lifetime as Dean of the Graduate Library School at University of Chicago,as winner of the American Society for Information Science Award of Merit for 2000,and as author of many seminal articles.In this informal essay,I will give my personal perspective on Don's contributions to science,and outline some current and future directions in literature-based discovery that are rooted in concepts that he developed.Design/methodology/approach: Personal recollections and literature review.Findings: The Swanson A-B-C model of literature-based discovery has been successfully used by laboratory investigators analyzing their findings and hypotheses.It continues to be a fertile area of research in a wide range of application areas including text mining,drugrepurposing,studies of scientific innovation,knowledge discovery in databases,and bioinformatics.Recently,additional modes of discovery that do not follow the A-B-C model have also been proposed and explored(e.g.so-called storytelling,gaps,analogies,link prediction,negative consensus,outliers,and revival of neglected or discarded research questions).Research limitations: This paper reflects the opinions of the author and is not a comprehensive nor technically based review of literature-based discovery.Practical implications: The general scientific public is still not aware of the availability of tools for literature-based discovery.Our Arrowsmith project site maintains a suite of discovery tools that are free and open to the public(http://arrowsmith.psych.uic.edu),as does BITOLA which is maintained by Dmitar Hristovski(http://http://ibmi.mf.uni-lj.si/bitola),and Epiphanet which is maintained by Trevor Cohen(http://epiphanet.uth.tmc.edu/).Bringing user-friendly tools to the public should be a high priority,since even more than advancing basic research in informatics,it is vital that we ensure that scientists actually use discovery tools and that these are actually able to help them make experimental discoveries in the lab and in the clinic.Originality/value: This paper discusses problems and issues which were inherent in Don's thoughts during his life,including those which have not yet been fully taken up and studied systematically.

Yarn Quality Prediction and Diagnosis Based on Rough Set and Knowledge-Based Artificial Neural Network

In the spinning process, some key process parameters( i. e.,raw material index inputs) have very strong relationship with the quality of finished products. The abnormal changes of these process parameters could result in various categories of faulty products. In this paper, a hybrid learning-based model was developed for on-line intelligent monitoring and diagnosis of the spinning process. In the proposed model, a knowledge-based artificial neural network( KBANN) was developed for monitoring the spinning process and recognizing faulty quality categories of yarn. In addition,a rough set( RS)-based rule extraction approach named RSRule was developed to discover the causal relationship between textile parameters and yarn quality. These extracted rules were applied in diagnosis of the spinning process, provided guidelines on improving yarn quality,and were used to construct KBANN. Experiments show that the proposed model significantly improve the learning efficiency, and its prediction precision is improved by about 5. 4% compared with the BP neural network model.

KDD中规则提取的收敛网络方法及其应用

提出一种新的基于神经网络的规则提取方法 .提出的网络由一个主网络及其映射网络组成 ,具有二次收敛过程 .通过主网络的学习 (第 1次收敛 )完成知识学习和网络构造 ,在此基础上构造了其网络映射 ,通过该映射网络的收敛过程实现规则的提取 .该方法在规则提取时无须遍历解空间 ,从而很好地提高了搜索效率 ,降低了计算复杂度 .同时 ,还提出估计规则数下限的信度差方法 .

Ranking of E-Health Barriers Faced by Saudi Arabian Citizens, Healthcare Professionals and IT Specialists in Saudi Arabia

Many boundaries are hindering successful utilisation of e-health in the Kingdom of Saudi Arabia (KSA). We have previously proposed an integrated framework of knowledge management and knowledge discovery to overcome barriers of e-health in KSA. Our proposed framework facilitates diabetes self-management for diabetic citizens in the Kingdom. In this paper, we will investigate and rank the barriers of e-health in KSA from the prospective of three stakeholders. We designed a questionnaire which constituted of items related to eight different e-health barriers and its associated sub-barriers. Citizens participated in 51 items related to six barriers. Healthcare professionals answered 83 items related to eight barriers. IT specialists participated in 74 items related to six barriers. Within each group of respondents, we compared the mean scores for each factor and sub-factor. The highest possible score for the mean was 5.00 and the lowest was 0.00 where the higher the mean score was the more the barrier constituted an obstacle for e-health in KSA. Citizens ranked the connectivity of information system as the top barrier with the mean of 4.0 whereas the least barrier was the cultural barriers with the mean score of 3.1. Healthcare professionals ranked the connectivity of information systems as the top barriers with the mean score of 3.5 whereas the least barrier was the technical expertise and computer skills with the mean score of 2.2. The top ranked barrier from the perspective of IT specialists was the medication safety with the mean score of 3.5 and the least ranked barrier was security and privacy with the mean score of 2.2. The results showed consistency with the literature review. Our proposed framework will contribute to the successful implementation of e-health initiatives and assist citizens in KSA to self- manage diabetes.

Risk pre-warning of tender evaluation for civil projects:an outlier detection model

The marking scheme method removes the low scores of the contractor's attributes given by experts when the overall score is calculated, which may result in that a contractor with some latent risks will win the project. In order to remedy the above defect of the marking scheme method, an outlier detection model, which is one mission of knowledge discovery in data, is established on the basis of the sum of similar coefficients. Then, the model is applied to the historical score data of tender evaluation for civil projects in Tianjin, China, according to which the outliers of the scores of the contractor's attributes can be detected and analyzed. Consequently, risk pre-warning can be carried out, and some advice to employers can be given to prevent some latent risks and help them improve the success rate of bidding projects.

Using Machine Reading to Understand Alzheimer's and Related Diseases from the Literature

Purpose: This paper aims to better understand a large number of papers in the medical domain of Alzheimer's disease(AD) and related diseases using the machine reading approach.Design/methodology/approach: The study uses the topic modeling method to obtain an overview of the field,and employs open information extraction to further comprehend the field at a specific fact level.Findings: Several topics within the AD research field are identified,such as the Human Immunodeficiency Virus(HIV)/Acquired Immune Deficiency Syndrome(AIDS),which can help answer the question of how AIDS/HIV and AD are very different yet related diseases.Research limitations: Some manual data cleaning could improve the study,such as removing incorrect facts found by open information extraction.Practical implications: This study uses the literature to answer specific questions on a scientific domain,which can help domain experts find interesting and meaningful relations among entities in a similar manner,such as to discover relations between AD and AIDS/HIV.Originality/value: Both the overview and specific information from the literature are obtained using two distinct methods in a complementary manner.This combination is novel because previous work has only focused on one of them,and thus provides a better way to understand an important scientific field using data-driven methods.

Concept Lattice Construction through the Composition and Decomposition of Formal Contexts

The purpose of this paper is to study the construction of concept lattice from variable formal contexts.Composition and decomposition theories are proposed for the unraveling of concept lattice from contexts with variable attribute set in the process of information updating.The relationship between the extension sets of the original context and that of its sub-context is analyzed.The composition and decomposition theories are then generalized to the situation involving more than two sub-contexts and the situation with variable attribute set and object set.

Designing a Model to Study Data Mining in Distributed Environment

To make business policy, market analysis, corporate decision, fraud detection, etc., we have to analyze and work with huge amount of data. Generally, such data are taken from different sources. Researchers are using data mining to perform such tasks. Data mining techniques are used to find hidden information from large data source. Data mining is using for various fields: Artificial intelligence, Bank, health and medical, corruption, legal issues, corporate business, marketing, etc. Special interest is given to associate rules, data mining algorithms, decision tree and distributed approach. Data is becoming larger and spreading geographically. So it is difficult to find better result from only a central data source. For knowledge discovery, we have to work with distributed database. On the other hand, security and privacy considerations are also another factor for de-motivation of working with centralized data. For this reason, distributed database is essential for future processing. In this paper, we have proposed a framework to study data mining in distributed environment. The paper presents a framework to bring out actionable knowledge. We have shown some level by which we can generate actionable knowledge. Possible tools and technique for these levels are discussed.

An Experimental Analysis of the Applications of Datamining Methods on Bigdata

Data mining is a procedure of separating covered up,obscure,however possibly valuable data from gigantic data.Huge Data impactsly affects logical disclosures and worth creation.Data mining(DM)with Big Data has been broadly utilized in the lifecycle of electronic items that range from the structure and generation stages to the administration organize.A far reaching examination of DM with Big Data and a survey of its application in the phases of its lifecycle won't just profit scientists to create solid research.As of late huge data have turned into a trendy expression,which constrained the analysts to extend the current data mining methods to adapt to the advanced idea of data and to grow new scientific procedures.In this paper,we build up an exact assessment technique dependent on the standard of Design of Experiment.We apply this technique to assess data mining instruments and AI calculations towards structure huge data examination for media transmission checking data.Two contextual investigations are directed to give bits of knowledge of relations between the necessities of data examination and the decision of an instrument or calculation with regards to data investigation work processes.

Planform Dependency on Airfoil Design Results for Supersonic Wing in Supersonic and Transonic

In this study, the wing design problem for different planforms for supersonic transport (SST) under supersonic and transonic cruise conditions is discussed to obtain knowledge of the supersonic air-foil from the viewpoint of wing planform dependency. Two types of planforms were considered—a cranked arrow wing with a high sweep-back angle and a tapered wing with a low sweep- back angle. The optimum airfoils of these planforms were designed by efficient global optimization, which combined the evolutionary algorithm with the Kriging surrogate model. To acquire design knowledge, the functional analysis of variance was applied to the solution space and the design space. The design results show that the optimum airfoil and the contribution ratios of design variables for the airfoils of the two planform are different.

Microbial Dark Matter: from Discovery to Applications

With the rapid increase of the microbiome samples and sequencing data,more and more knowledge about microbial communities has been gained.However,there is still much more to learn about microbial communities,including billions of novel species and genes,as well as countless spatiotemporal dynamic patterns within the microbial communities,which together form the microbial dark matter.In this work,we summarized the dark matter in microbiome research and reviewed current data mining methods,especially artificial intelligence(AI)methods,for different types of knowledge discovery from microbial dark matter.We also provided case studies on using AI methods for microbiome data mining and knowledge discovery.In summary,we view microbial dark matter not as a problem to be solved but as an opportunity for AI methods to explore,with the goal of advancing our understanding of microbial communities,as well as developing better solutions to global concerns about human health and the environment.

Product portfolio map:a visual tool for supporting product variant discovery and structuring

A fundamental criterion for reusing and continuously improving knowledge in product development is ensuring that the knowledge is explicit and visual.This paper is based on the situation of an engineer-to-order(ETO) manufacturing company,where historically grown product variety and related knowledge are diffuse(tacit).Consequently,several resources are used in(re)developing derivatives of previous products rather than innovating new ones.To establish a more competitive configure-to-order(CTO) product strategy,product knowledge needs to be revealed,systemized,and structured,and thus made explicit.Hence,product-specific knowledge and product variants have been analyzed and subsequently mapped at architectural,functional,and physical levels in one unified map and tested in the form of a proof-of-concept(POC)demonstrator with the introduced SME company.The result is a product portfolio map that forms a base for defining a systemized,transparent,unified product variant overview,which can be used as a basis for implementing a cross-variant product architecture and supporting knowledge-based approaches.

Causality in structural engineering: discovering new knowledge by tying induction and deduction via mapping functions and explainable artificial intelligence

Causality is the science of cause and effect.It is through causality that explanations can be derived,theories can be formed,and new knowledge can be discovered.This paper presents a modern look into establishing causality within structural engineering systems.In this pursuit,this paper starts with a gentle introduction to causality.Then,this paper pivots to contrast commonly adopted methods for inferring causes and effects,i.e.,induction(empiricism)and deduc-tion(rationalism),and outlines how these methods continue to shape our structural engineering philosophy and,by extension,our domain.The bulk of this paper is dedicated to establishing an approach and criteria to tie principles of induction and deduction to derive causal laws(i.e.,mapping functions)through explainable artificial intelligence(XAI)capable of describing new knowledge pertaining to structural engineering phenomena.The proposed approach and criteria are then examined via a case study.

Functional annotation map of natural compounds in traditional Chinese medicines library: TCMs with myocardial protection as a case

The chemical complexity of traditional Chinese medicines(TCMs) makes the active and functional annotation of natural compounds challenging. Herein, we developed the TCMs-Compounds Functional Annotation platform(TCMs-CFA) for large-scale predicting active compounds with potential mechanisms from TCM complex system, without isolating and activity testing every single compound one by one. The platform was established based on the integration of TCMs knowledge base, chemome profiling, and high-content imaging. It mainly included:(1) selection of herbal drugs of target based on TCMs knowledge base;(2) chemome profiling of TCMs extract library by LC-MS;(3) cytological profiling of TCMs extract library by high-content cell-based imaging;(4) active compounds discovery by combining each mass signal and multi-parametric cell phenotypes;(5) construction of functional annotation map for predicting the potential mechanisms of lead compounds. In this stud TCMs with myocardial protection were applied as a case study, and validated for the feasibility and utility of the platform. Seven frequently used herbal drugs(Ginseng, etc.) were screened from 100,000 TCMs formulas for myocardial protection and subsequently prepared as a library of 700 extracts. By using TCMs-CFA platform, 81 lead compounds, including 10 novel bioactive ones, were quickly identified by correlating 8089mass signals with 170,100 cytological parameters from an extract library. The TCMs-CFA platform described a new evidence-led tool for the rapid discovery process by data mining strategies, which is valuable for novel lead compounds from TCMs. All computations are done through Python and are publicly available on GitHub.

Big Earth Data:a new challenge and opportunity for Digital Earth’s development

Digital Earth has seen great progress during the last 19 years.When it entered into the era of big data,Digital Earth developed into a new stage,namely one characterized by‘Big Earth Data’,confronting new challenges and opportunities.In this paper we give an overview of the development of Digital Earth by summarizing research achievements and marking the milestones of Digital Earth’s development.Then,the opportunities and challenges that Big Earth Data faces are discussed.As a data-intensive scientific research approach,Big Earth Data provides a new vision and methodology to Earth sciences,and the paper identifies the advantages of Big Earth Data to scientific research,especially in knowledge discovery and global change research.We believe that Big Earth Data will advance and promote the development of Digital Earth.