DATA MINING PREPARATION: PROCESS, TECHNIQUES AND MAJOR ISSUES IN DATA ANALYSIS

The development of an Electronic Health Record (EHR) system requires a well-defined data set and a robust data model to support complex clinical workflows, interoperability, and data quality. Many existing EHR implementations lack standardized and integrated data models, resulting in redundancy, semantic inconsistency, and limited support for secondary data use. This study aims to develop a comprehensive EHR data model based on a Minimum Data Set (MDS) that systematically represents medical service processes across emergency, outpatient, inpatient, and surgical care. The proposed approach involves the design of a conceptual, logical, and physical data model. Business processes were first analysed to identify relationships among clinical and administrative data classes, which were then transformed into a conceptual data model. The logical data model was developed through database normalization and schema definition, while the physical data model was constructed to represent the actual database structure for implementation. The results demonstrate a structured EHR data model comprising 21 interrelated data classes that reflect real-world medical service workflows. The conceptual data model establishes clear entity relationships, supporting data consistency and interoperability. The logical data model provides a normalized SQL-based schema that minimizes redundancy and enhances data integrity, while the physical data model enables efficient data storage and access. A prototype EHR system was implemented to evaluate the feasibility of the proposed data model, showing stable performance, usability, and alignment with functional requirements. This study concludes that a standardized EHR data model derived from an MDS can effectively support comprehensive clinical documentation, interoperability, and future scalability. The proposed model provides a strong foundation for developing national or institutional EHR systems and can serve as a reference for further EHR data standardization efforts.

Big geospatial raster data pose a grand challenge to data management technologies for effective big data query and processing. To address these challenges, various big data container solutions have been developed or enhanced to facilitate data storage, retrieval, and analysis. Data containers were also developed or enhanced to handle geospatial data. For example, Rasdaman was developed to handle raster data and GeoSpark/SpatialHadoop were enhanced from Spark/Hadoop to handle vector data. However, there are few studies to systematically compare and evaluate the features and performances of these popular data containers. This paper provides a comprehensive evaluation of six popular data containers (i.e., Rasdaman, SciDB, Spark, ClimateSpark, Hive, and MongoDB) for handling multi-dimensional, array-based geospatial raster datasets. Their architectures, technologies, capabilities, and performance are compared and evaluated from two perspectives: (a) system design and architecture (distributed architecture, logical data model, physical data model, and data operations); and (b) practical use experience and performance (data preprocessing, data uploading, query speed, and resource consumption). Four major conclusions are offered: (1) no data containers, except ClimateSpark, have good support for the HDF data format used in this paper, requiring time- and resource-consuming data preprocessing to load data; (2) SciDB, Rasdaman, and MongoDB handle small/mediate volumes of data query well, whereas Spark and ClimateSpark can handle large volumes of data with stable resource consumption; (3) SciDB and Rasdaman provide mature array-based data operation and analytical functions, while the others lack these functions for users; and (4) SciDB, Spark, and Hive have better support of user defined functions (UDFs) to extend the system capability.

ABSTRACTMore than 50 years ago, John Tukey called for a reformation of academic statistics. In “The Future of Data Analysis,” he pointed to the existence of an as-yet unrecognized science, whose subject of interest was learning from data, or “data analysis.” Ten to 20 years ago, John Chambers, Jeff Wu, Bill Cleveland, and Leo Breiman independently once again urged academic statistics to expand its boundaries beyond the classical domain of theoretical statistics; Chambers called for more emphasis on data preparation and presentation rather than statistical modeling; and Breiman called for emphasis on prediction rather than inference. Cleveland and Wu even suggested the catchy name “data science” for this envisioned field. A recent and growing phenomenon has been the emergence of “data science” programs at major universities, including UC Berkeley, NYU, MIT, and most prominently, the University of Michigan, which in September 2015 announced a $100M “Data Science Initiative” that aims to hire 35 new faculty. Teaching in these new programs has significant overlap in curricular subject matter with traditional statistics courses; yet many academic statisticians perceive the new programs as “cultural appropriation.” This article reviews some ingredients of the current “data science moment,” including recent commentary about data science in the popular media, and about how/whether data science is really different from statistics. The now-contemplated field of data science amounts to a superset of the fields of statistics and machine learning, which adds some technology for “scaling up” to “big data.” This chosen superset is motivated by commercial rather than intellectual developments. Choosing in this way is likely to miss out on the really important intellectual event of the next 50 years. Because all of science itself will soon become data that can be mined, the imminent revolution in data science is not about mere “scaling up,” but instead the emergence of scientific studies of data analysis science-wide. In the future, we will be able to predict how a proposal to change data analysis workflows would impact the validity of data analysis across all of science, even predicting the impacts field-by-field. Drawing on work by Tukey, Cleveland, Chambers, and Breiman, I present a vision of data science based on the activities of people who are “learning from data,” and I describe an academic field dedicated to improving that activity in an evidence-based manner. This new field is a better academic enlargement of statistics and machine learning than today’s data science initiatives, while being able to accommodate the same short-term goals. Based on a presentation at the Tukey Centennial Workshop, Princeton, NJ, September 18, 2015.

The development of modern technology allows enterprises to continuously expand the means of financial management. The use of advanced information technology and modern management means makes the financial management of enterprises continue to improve, but there are also many problems. In the face of various types of risks that enterprises may face at present, in addition to strengthening financial accounting, enterprises should also recognize the various risks that the double-edged sword of financial management informatization may bring to enterprise financial management and carefully analyze the reasons for the emergence of risks, from the ideological and technical means to continuously improve the entire process of financial management informatization, which is an important topic for every financial worker to discuss. This paper mainly studies and innovates the data mining process and the support vector machine model and designs the data preprocessing method in the data mining process to perform feature selection and optimize the parameters of the support vector machine model. The specific process is as follows: based on CRISP-DM, the industry-standard model of the data mining process, a series of data preprocessing, eigenvalue extraction, parameter optimization, training set pruning, and other methods are designed from business understanding, data understanding, data preparation, and other aspects to improve data quality. In the traditional support vector machine, when the test sample is located at the boundary point of the hyperplane, the judgment may be wrong. In the aspect of SVM model improvement, according to the discrimination method of SVM, the weighted K-nearest neighbor algorithm is introduced to redistinguish the qualified test samples in the feature space. From the whole process of data mining, a data mining system is designed, which is “data preprocessing standardization + genetic algorithm feature selection + training set pruning + support vector machine classifier discrimination optimization.” Finally, this paper uses a series of data mining optimization methods designed to mine the actual financial data of listed companies and has achieved good results.

This research is mainly focused on to talk about data preparation, what better way to start than from observation. Everyone is familiar with the adage that a data scientist should spend 80% of his or her time preparing the data and just 20% actually working with it, particularly when it comes to visualization. This essay will concentrate on data preparation, including the most common issues, solutions, and developments. Data must be put into the proper form before analysis can be done on it. Data manipulation and organization are steps in the preparation of data for analysis. Iteratively transforming unstructured, chaotic raw data into a more organized, practical form that is ready for further analysis is known as data preparation. Data profiling, cleaning, integration, and transformation are just a few of the primary activities (or tasks) that make up the entire preparation process.

Data Scientist - The New ProfessionData scientist is probably the most trendy job in Information Technology (IT) nowadays. This new profession emerged with the Big Data wave. Even though there is no such thing like an exact job profile, we expect that the data scientist can handle all the Big Data challenges that are novel to us. Without being a magician she or he shall help to deliver us all the magic Big Data promises. The data scientist capitalizes on unstructured data without taking the roles of a programmer, database expert, statistician, or content manager. All these professions are around for decades. So, why invent a new one?More than anything, what data scientists do is make discoveries while swimming in data [...] they are able to bring structure to large quantities of formless data and make analysis possible. [3] They sketch, orchestrate, and control the discovery process. The leading paradigm in this process it to find information that meets a certain need or provides an answer for a certain problem. need to avoid the temptation of following a data-driven approach instead of a problem-driven one. [5] The data scientist has to develop an idea about the required information that meets our information need. We can expect that data scientists have a deep understanding of the foundations of both, the nature of information and the domain. They follow a mental model on the information demand that abstractly reflects the facts they expect to encounter in data and the way to detect and present them to us. We expect the data scientist to discover novel information that may provide us with new insights. And we want these insights to be true. It is thus part of the data scientist's responsibility to make sure that the discovered information is not only novel but also trustworthy. The data scientists cannot prove data analysis models. That exceeds their capabilities. We cannot hold them liable for information that eventually turns out to be wrong. Nevertheless their skills should include a sound sensation of plausibility that helps them to raise doubts and to prompt a closer look when the results of data analysis seem too questionable to them. However, separating questionable results from plausible ones is a task that is far from being trivial.Separating Data Science from Data FictionWhen driving a car we often encounter these nice roadside signs that sometimes make us realize that we're driving too fast. Furthermore, on some occasions there is official personnel not far from these signs measuring our speed, explaining to us our traffic infraction face-to-face and documenting it on a speeding ticket. Doesn't this sound a bit outdated? There are enough sensors in a car that measure location, speed, time, and more. Even if the car's sensors don't measure those, the driver's cellphone can do it. Combining this sensor information with the cartographic data about roads and speed limits we can easily imagine that, by the end of day, the car or the phone exactly knows every infraction we committed and can automatically trigger the issuing of speeding tickets. Aren't there just legal aspects that hamper this technically feasible scenario making its way into reality?One trait of Big Data is the availability of sensor data and their combination with already available data generating new information, like the detection of traffic infraction. We can extrapolate this scenario in many directions, including more types of infraction, driving for an irresponsibly long time without a substantial break, or detecting patterns of aggressive driving. We can extend it also towards future possibilities if we think about sensor information from smart watches indicating a possibly problematic pulse rate or from the car's air control sensor that the driver may drive under the influence of alcohol.Much like in Data Mining, the strength of Big Data originates from the combination of facts producing new information. …

The cultivation of high-quality skilled talents is an important subject that needs to be improved in the current vocational college education. Through the construction of the training mechanism corresponding to the specialty, the students’ practical ability can be promoted. Driven by big data, many technical enterprises gradually strengthen the recruitment of technical talents. However, due to the wide distribution of talents, data mining is more difficult. Therefore, it introduces the data mining system and construction analysis of skilled talents based on big data. This paper first analyzes the demand of skilled talents; secondly, the popular data mining technology is applied to this big data system, and the reasons for the low efficiency of data analysis and processing of skilled talents are obtained; then, the Bayesian algorithm in data mining technology is used to classify the technical talents, so that the enterprises in need can select and locate the skilled talents Data positioning is also carried out. Finally, it is concluded that the system can effectively promote the recruitment of technical talents in enterprises, and the ability level of these people after entering the company also meets the post requirements.

with the development of computer information science and technology, the acquirement and access of data from huge database become more and more convenient. Not all the algorithm of different database is the same. Data mining finds the method of characteristic demand data from a large amount of information. It searches data according to relevance and clustering of data. This paper presents a new data mining system---B / S framework and establishes rules of data mining algorithms and mathematical models using fuzzy membership function and Apriority algorithm. This paper establishes a set of data mining system related with subjects learn of driving school taking the driving school for example and using multi-layer B / S framework mathematical model and results of driving candidates. It also finds the importance of each study subject. It proposes data reference for the order of the learn subjects of driving school which provides a theoretical reference for the study of data mining algorithms and systems.

Data Mining refers to a set of methods applicable to large and complex databases to eliminate the randomness and discover the hidden pattern. Datamining (DM), also known as knowledge discovery from databases (KDD), is the extraction of new knowledge from huge databases. Data mining involves the use of sophisticated data analysis tools to discover previously unknown, valid patterns and relationships in large data sets. Data mining tools can forecast the future trends and activities to support the decision of people. The scope of datamining is associated with Uncovering trends and patterns are a great power for the businesses of all sectors and industries. Modern intrusion detection applications are confronted with a variety of issues. These applications must be reliable, extensible, manageable, and minimal in maintenance costs. Data mining-based intrusion detection systems (IDSs) have shown high accuracy, good generalisation to novel types of intrusion, and stable behaviour in a changing environment in recent years. The number of hidden layers in various neural network topologies is evaluated in order to discover the best neural network. The technique of attempting to discover instances of network attacks by comparing current behaviour to the expected actions of an intruder is known as misuse detection. Artificial neural networks have the ability to detect and classify network activity using data that is limited, incomplete, and nonlinear. The main purpose of this work is to identify privacy and security concerns among cloud computing participants and consumers in a distributed environment. Techniques like Machine Learning, Natural Language Processing (NLP), and Data Mining are combined to automatically identify and uncover patterns from many sorts of materials. Predictive analytics is capable of dealing with both continuous and discontinuous changes. Classification, prediction, and to some extent, affinity analysis constitute the analytical methods employed in predictive analytics. The contemporary study in text or document mining is focusing on syntactic components and the semantic environment. In order to accomplish this, and with the motivation gained from our previous research contributions, we investigated a mining model to classify documents based on the Order of Context, Concept, and Semantic Relations (OCCSR). The use of data mining techniques based on Cloud computing will enable users to retrieve meaningful information from virtually integrated data warehouses, lowering infrastructure and storage costs. Data mining can extract useful and potentially useful information from the cloud. Big Data is typically defined by three characteristics known as the 3Vs (Volume, Velocity and Variety). The surveys approaches, environments, and technologies in key areas for Big Data analytics capabilities and discusses how they aid in the development of analytics solutions for Clouds. The clustering technique belongs to an unsupervised learning and it is used to discover a new set of categories. Grid-based clustering has the shortest processing time, which is typically determined by the size of the grid rather than the data. We compare the performance of three clustering algorithms: hierarchical clustering, density-based clustering, and K Means clustering. The majority of current approaches to detecting misuse involve the use of rule-based expert systems to identify indicators of known attacks. We provide a brief overview of the use of various Artificial Intelligence techniques and their advancements in the design, development, and application of Intrusion Detection Systems (IDS) for protecting computer and communication networks from intruders. The goal of Knowledge Discovery in Data (KDD) is to extract information that is not obvious by using careful and detailed analysis and interpretation. To drive decisions and actions, analytics employs KDD, data mining, text mining, statistical and quantitative analysis, explanatory and predictive models, and advanced and interactive visualisation techniques.

Data preparation is often cited as the most time consuming phase of a Knowledge Discovery and Data Mining (KDDM) process. This is attributed to the fact that this phase is highly dependent on the expertise of the analyst. Although process models exist for KDDM the description of their phases of the process focus on outlining what must be done but often do not detail how this should be done. While there is some research in addressing the how of the phases, the data preparation phase is thought to be the most challenging and is often described as an art rather than a science. The tasks defined in this phase are thought to be highly dependent on the expertise of the analyst and the context. While we are of the view that there will always be an art to data preparation we will demonstrate that the science can actually enhance the art. We further contend that as more research of this kind is published, that demonstrates a variety of data preparation techniques that enhance the data mining process, the more effective will be the science of data preparation.

Extraction, Transformation and Loading (ETL) is a key process of data warehouse building. It integrates data sources with diverse features and structures. Numerous approaches and implementations of ETL have been introduced. However, they still have the following disadvantages: human-dependence, information integration only in syntactic levels, incomplete the homogeneity solution, difficulty to install and configure, etc. In this paper, we propose an alternative approach to the ETL process by attacking the homogeneity in data sources with an ontology-based methodology. Our approach can overcome the drawbacks of most existing approaches; as it automates the key activities of the process, such as: extraction of metainformation, generation of logical and physical data models, and transformation of information.

Intelligent vehicle monitoring system is collecting and processing the large amounts of information all the time. The paper applies data warehouse technique to intelligent vehicle monitoring system to store and manage the information. Based on the analysis of the subject field in the system and the design of dimension table, the system logical model and physical data model, the data warehouse of system is built. And, the data in the data warehouse are analyzed and summarized by the data analysis and display tools. Decision-makers can clearly and intuitively see the result, and this can offer the decision foundation for development of China's transportation.

The geo-spatial database is a new technology in database systems which allow storing, retrieving and maintaining the spatial data.In this paper, we seek to design and implement a geo-spatial database for managing the traveler's demand with the aid of open-source tools and object-relational database package.The building of geo-spatial database starts with the design of data model in terms of conceptual, logical and physical data model and then the design has been implemented into an object-relational database.The geo-spatial database is developed to facilitate the storage of geographic information (where things are) with descriptive information (what things are like) into the vector model.The developed vector geo-spatial data can be accessed and rendered in the form of map to create the awareness of existence of various services and facilities for prospective travelers and visitors.

The purpose of the work is to study the current problems and prospects of the solution for processing big data received or stored in the Internet (web data), as well as the possibility of practical realization of Data Mining technology for big web data on practical example. Materials and methods. The study included a review of bibliographic sources on big data analysis problems.Data Mining technology was used to analyze large web data, as well as computer modeling of a practical problem using the C # programming language and creating a DDL database structure for accumulating web data.Results. In the course of the work, the specifics of big data were described, the main characteristics of big data were highlighted, and modern approaches to processing big data were analyzed. A brief description of the horizontal-scalable architecture and the BI-solution architecture for big data processing is given. The problems of processing large web data are formulated: limiting the speed of access to data, providing access via network protocols through general-purpose networks.An example showing the approach to processing large web data was also implemented. Based on the idea of big data, the described complexities of web data processing and the methods of Data Mining, techniques were proposed for effectively solving the practical problem of processing and searching patterns in a large data array.The following classes have been developed in the C # programming language:Class of receiving web data via the Internet; Data conversion class;Intelligent data processing class;Created DDL script that creates a structure for the accumulation of web data.A single UML class diagram has been developed.The constructed system of data and classes allows to solve the main part of the problems of processing large web data and perform intelligent processing using Data Mining technology in order to solve the problem posed of identifying certain records in a large array. The combination of object-oriented approach, neural networks and BI-analysis to filter data will speed up the process of data processing and obtaining the result of the studyConclusion. According to the results of the study, it can be argued that the current state of technology for analyzing large web data allows you to efficiently process data objects, identify patterns, get hidden data and get full-fledged statistical data.The obtained results can be used both for the purpose of the initial study of big data processing technologies, and as a basis for developing an already real application for analyzing web data. The use of neural networks and the created universal classes-handlers makes the created architecture flexible and self-learning, and the class declarations and the base DDL structure will greatly simplify the development of program code.

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.