Assessing reliability of Big Data Knowledge Discovery process

Notice of Violation of IEEE Publication Principles<br><br>"Knowledge Discovery from Virtual Enterprise Model Based on Semantic Annotation,"<br>by Chengzhu Sun, Xiaofei Xu, Xiangyang Li, Shengchun Deng,<br>in the Proceedings of the Fifth International Conference on Fuzzy Systems and Knowledge Discovery, 2008. FSKD '08, vol.5, pp.546-551, Oct. 2008<br><br>After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.<br><br>This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.<br><br>"A Controlled Language for Semantic Annotation and Interoperability in e-Business Applications"<br>by M.Missikoff, F.Schiappelli , F.Taglino<br>in the Proceedings of the Semantic Integration Workshop, collocated with the Second International Semantic Web Conference (ISWC-03), October 2003<br><br> <br/> Discovering knowledge from virtual enterprise model is becoming increasingly important, as numerical models established for virtual enterprise are difficult to support interoperability of virtual enterprise. To solve this problem, a knowledge discovery method based on semantic annotation is put forward in this paper. A graphic ontology representation schema for virtual enterprise model is described, which is called ontology structure graph (OSG). Based on applications of semantic annotation, the process of knowledge discovery in the virtual enterprise model base is given and activities such as model selection, semantic annotation, data transformation, knowledge extraction and ontology interoperation in knowledge discovery process are illustrated in detail. Then, several critical issues influencing knowledge discovery are explained, including the organization of domain vocabulary, the definition of semantic annotation rules and semantic affinity function, the formulation of reference ontology. Finally, an instance is given to demonstrate the knowledge discovery method and results of knowledge discovery are presented.

It is our great pleasure to welcome you to the 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). The annual ACM SIGKDD conference is the premier international forum for data mining, knowledge discovery and big data. It brings together researchers and practitioners from academia, industry, and government to share their ideas, research results and experiences. KDD-2013 features plenary presentations, paper presentations, poster sessions, workshops, tutorials, exhibits, demonstrations, and the KDD Cup competition. Today, you hear a lot about big data, data science and data intensive computing. The core of this work is extracting knowledge and useful information from data, which for science leads to beautiful insights, and for applications leads to actions, alerts and decisions. The KDD community has always been at the center of this activity and it is clear from this conference that it will continue to drive this broader field of big data. This year there were 726 submissions to the KDD Research Track, and 125 papers were accepted. There were 136 submissions to the KDD Industry and Government Track, and 34 papers were accepted. KDD also has a history of inviting talks that are of broad interest to the KDD community. This year we chose to have 4 plenary talks. A program committee also selected 8 talks to present at the Industry Practice Exposition. A strength of the KDD conference is the number of workshops and tutorials that are co-located with it. This year there were 10 full-day workshops, 5 half-day workshops, and 6 tutorials. We thank all sponsors, who are a very important part of the conference, and the members of the Organizing Committee and our other colleagues who volunteered their time during the past year to make this conference a success. Special thanks goes to the Research Track Co-Chairs and the Industry and Government Track Co-Chairs. Also special thanks are due to the Local Arrangements Chair, the Treasurer, the Proceedings Co-Chairs, and the KDD Cup Committee. We are grateful to the several program committees that provided the advice necessary to put together a quality program - the Research Track Program Committee, the Research Track Senior Program Committee, the Industry and Government Track Program Committee, the Industry Practice Expo Program Committee, the Workshop Program Committee, the Tutorial Program Committee, and the Demo Program Committee. We know that you will find this year's exhibits and demonstrations exciting and remind you that some of the most interesting discussions can be found there. Please join us for KDD-2013 to gain new knowledge and to exchange exciting new research results, leading practices, and high impact applications in big data, knowledge discovery and data mining. We hope that you will find this program interesting and thought-provoking and that the conference will provide you with a valuable opportunity to share ideas with other researchers and practitioners from institutions around the world.

Applied data science in patient-centric healthcare: Adaptive analytic systems for empowering physicians and patients

Knowledge discovery in databases is a process that aims at the discovery of associations within data sets. The analysis of geo-referenced data demands a particular approach in this process. This chapter presents a new approach to the process of knowledge discovery, in which qualitative geographic identifiers give the positional aspects of geographic data. Those identifiers are manipulated using qualitative reasoning principles, which allows for the inference of new spatial relations required for the data mining step of the knowledge discovery process. The efficacy and usefulness of the implemented system — Padrão — has been tested with a bank dataset. The results support that traditional knowledge discovery systems, developed for relational databases and not having semantic knowledge linked to spatial data, can be used in the process of knowledge discovery in geo-referenced databases, since some of this semantic knowledge and the principles of qualitative spatial reasoning are available as spatial domain knowledge.

Adding the dimension of time to databases produces time series databases (TSDB) and introduces new aspects and difficulties to data mining and knowledge discovery. In this correspondence, we introduce a general methodology for knowledge discovery in TSDB. The process of knowledge discovery in TSDR includes cleaning and filtering of time series data, identifying the most important predicting attributes, and extracting a set of association rules that can be used to predict the time series behavior in the future. Our method is based on signal processing techniques and the information-theoretic fuzzy approach to knowledge discovery. The computational theory of perception (CTP) is used to reduce the set of extracted rules by fuzzification and aggregation. We demonstrate our approach on two types of time series: stock-market data and weather data.

Big data applications are becoming more popular across many different fields and civil engineering is no different. The benefits of big data lie in its potential to provide valuable insights into various large datasets. Big data applications can identify patterns and trends that were previously unknown, which can help them make informed decisions and develop effective strategies. In the case of civil engineering, this could be taking large datasets that have been produced in relation to pieces of infrastructure and using them to create more efficient management strategies. One of the issues with using big data is that if the input dataset is flawed then the output resulting from any big data analysis will be compromised. Therefore, data validation is used, which is the process of ensuring that data is accurate, complete, and consistent. The consequence of not undertaking data validation may be Inaccurate or inconsistent data which can lead to incorrect insights and decisions in big data applications. This paper explores the necessity of validating data before it is used in a big data application. It outlines some of the different methods used for validating data and provides an overview of the potential issues that may arise from data validation errors. A case study is then presented showing the process of validation on data collected from four bridges and provides recommendations for implementing data validation as part of a larger big data workflow. The results of the case study show that validation of data is an important step in the big data process both for confidence in the outputs and to make big data applications more useful and more common in the civil engineering field. The discussion and presented case study in this paper highlight the necessity of validating data. It has shown some of the potential issues that may arise from not undertaking data validation.

The field of Big Data is rapidly developing with a lot of ongoing research, which will likely continue to expand in the future. A crucial part of this is Knowledge Discovery from Data (KDD), also known as the Knowledge Discovery Process (KDP). This process is a very complex procedure, and for that reason it is essential to divide it into several steps (Figure Some authors use five steps to describe this procedure, whereas others use only four. We use the following four-step description:

The objective of this book is to introduce the basic concepts of big data computing and then to describe the total solution of big data problems using HPCC, an open-source computing platform. The book comprises 15 chapters broken into three parts. The first part, Big Data Technologies, includes introductions to big data concepts and techniques; big data analytics; and visualization and learning techniques. The second part, LexisNexis Risk Solution to Big Data, focuses on specific technologies and techniques developed at LexisNexis to solve critical problems that use big data analytics. It covers the open source High Performance Computing Cluster (HPCC Systems) platform and its architecture, as well as parallel data languages ECL and KEL, developed to effectively solve big data problems. The third part, Big Data Applications, describes various data intensive applications solved on HPCC Systems. It includes applications such as cyber security, social network analytics including fraud, Ebola spread modeling using big data analytics, unsupervised learning, and image classification. The book is intended for a wide variety of people including researchers, scientists, programmers, engineers, designers, developers, educators, and students. This book can also be beneficial for business managers, entrepreneurs, and investors.

Analysing datasets using data mining techniques can enhance decision making in organizations. However, to ensure that the full potential of these techniques is realised it is important that decision makers understand there are Knowledge Discovery and Data Mining (KDDM) processes that are mature enough to be adopted. This paper demonstrates the benefits of using a KDDM process to evaluate survey data for internet banking users in Jamaica which includes demographic as well as attitudinal and behavioral variables. The major benefits of following this process include the selection of a set of models, rather than a single model, which are more relevant to the business/research objectives and use of a more targeted knowledge discovery process as the data mining analyst is now directed to consider the effects the decisions in each phase will have on subsequent phases. This leads to more relevant knowledge being extracted from the data mining process.

The voluminous amount of data stored in databases contains hidden knowledge which could be valuable to improve decision making process of any organization. As it is not humanely possible to analyze large databases, it has become essential to apply advanced data mining algorithms for extracting patterns (models) from data to support decision making. A number of data mining algorithms produce information of a statistical nature that allows the user to assess how accurate and reliable the discovered knowledge is? However, in many cases this is not enough for the users. Even if the discovered knowledge is highly accurate from a statistical point of view, it might not be interesting to the user. Therefore the process of knowledge discovery in databases (KDD) aims at discovering knowledge that is interesting and useful to the user. Most of the data mining algorithms so far have paid lot of attention to discovery of accurate and comprehensible knowledge. Though, the question of interestingness has been addressed time to time, it is being increasingly realized by data mining community that this subject needs a renewed focus. This paper is an attempt to review the measures of interestingness used in the data mining literature. The main contribution of the paper is to improve the understanding of interestingness measures for discovery of knowledge and identify the unresolved problems to set the directions for the future research in this area.

The knowledge discovery and data mining (KDDM) process models describe the various phases (e.g. business understanding, data understanding, data preparation, modeling, evaluation and deployment) of the KDDM process. They act as a roadmap for implementation of the KDDM process by presenting a list of tasks for executing the various phases. The checklist approach of describing the tasks is not adequately supported by appropriate tools, which specify ‘how’ the particular task can be implemented. This may result in tasks not being implemented. Another disadvantage is that the long checklist does not capture or leverage the dependencies that exist among the various tasks of the same and different phases. This not only makes the process cumbersome to implement, but also hinders possibilities for semi-automation of certain tasks. Given that each task in the process model serves an important goal and even affects the execution of related tasks due to the dependencies, these limitations are likely to negatively affect the efficiency and effectiveness of KDDM projects. This paper proposes an improved KDDM process model that overcomes these shortcomings by prescribing tools for supporting each task as well as identifying and leveraging dependencies among tasks for semi-automation of tasks, wherever possible.

Big data has evolved as a most challenging area in scientific study and research. It has drawn much attention during the last few years. It influences our modern society, business, government, healthcare, research and almost every discipline. In this data-driven era, where data is continuously acquired from a verity of sources for different purposes, the ability to make timely decisions based on available data is a very critical task. The massive data size, variety, velocity, accuracy and high dimensionality presents new challenges to big data. This paper attempts to present some challenges of big data. In addition, a study on the conceptual design of big data architecture presented on specific big data applications. In this paper, we presented research work on big data analytics techniques in the areas of text, sentiment, video, social media and predictive analytics. A comparative analysis on selected big data applications has been presented in detail.

Major challenge in the analysis of clinical data is to propose an integrated and modern access to the progressively increasing amounts of data in multiple formats, and efficient approaches for their management and processing. An approach to management of large amount of heterogeneous data sets from various data sources for a breast cancer diagnostic system is presented in this paper. Big genomic data architecture consists of data sources, storage, integration and preprocessing, real data stream, stream processing, analytical data store, analysis and reporting. Activities at data management for breast cancer diagnostic system are explained. Conceptual database architecture for storing data sets of several types in order to support breast cancer prediction is designed. The breast cancer database comprises of information related to breast cancer genes and functions - id, name, type, organism, function, and proteins coded, description, link for retrieving sequence. The patient's database consists of individual patient data - genetic data, clinical history, individual life style parameters, clinical tests results, environmental factors. The data sets in the suggested big data management system are retrieved from the biomedical research databases. The data management system is platform independent, easy to use and provides access to other databases such PubMed, NCBI. The purpose is to be used for data storage in a system for big data analytics and knowledge discovery, especially for the case study of breast cancer diagnostic. The advantages in data management, analysis, and knowledge discovery empower the scientists to achieve new scientific breakthroughs. As a result the research work is directed towards rapid management and processing of clinical data for solving problems in the field of precision medicine.

In this paper we deal with association rule mining in the context of a complex, interactive and iterative knowledge discovery process. After a general introduction covering the basics of association rule mining and of the knowledge discovery process in databases we draw the attention to the problematic aspects concerning the integration of both. Actually, we come to the conclusion that with regard to human involvement and interactivity the current situation is far from being satisfying. In our paper we tackle this problem on three sides: First of all there is the algorithmic complexity. Although today’s algorithms efficiently prune the immense search space the achieved run times do not allow true interactivity. Nevertheless we present a rule caching schema that significantly reduces the number of mining runs. This schema helps to gain interactivity even in the presence of extreme run times of the mining algorithms. Second, today the mining data is typically stored in a relational database management system. We present an efficient integration with modern database systems which is one of the key factors in practical mining applications. Third, interesting rules must be picked from the set of generated rules. This might be quite costly because the generated rule sets normally are quite large whereas the percentage of useful rules is typically only a very small fraction. We enhance the traditional association rule mining framework in order to cope with this situation.KeywordsAssociation RuleMining AlgorithmFrequent ItemsetsAssociation Rule MiningGeneralize Association RuleThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Time series data is a series of observation data accord- ing to a certain time sequence. It has been penetrate various field. This paper applies Rough set to the knowledge dis- covery of time series. The process of knowledge discovery in time series includes preprocessing of time series data, at- tributes selection and similarity sequence searching. Then, the time series is partitioned to a set of pattern(each pattern represents a trend of time series)by mobile window method. An information table is formed by the most important pre- dicting attributes and target attribute which in the trend- ing structure sequence identified from each pattern. This information table is suitable for the Rough set to discover knowledge. The extracted rules can predict the time series behavior in the future. We demonstrate our method on time series stock market data.