Data Mining Services Research Articles

The Application of Data Mining Technology in Data Service of Micro Service Architecture

This paper introduces the application of data mining technology in data service. Data mining is a new technology, but its application time is long, and the application effect is obvious. Data mining technology can be used in enterprise customer service system, which can help enterprises to find potential customers, while retaining the most valuable customers. We pointed out the necessity of establishing data mining technology and service intelligence analysis system based on the combination of intelligence analysis and service characteristics and processes, the method of data mining, knowledge management ideas applied to intelligence analysis and service system.

Release of the national healthcare big data in China: a historic leap in clinical research

At the beginning of 2017, the National scientific data sharing platform for population and health (NSDSPPH) released 49.1 TB data on Chinese population and health. The data comprise 280 million observations involving the fields of biomedicine, basic medicine, clinical medicine, public health, traditional Chinese medicine, pharmacology, population and reproductive health (http://www.ncmi.cn/1). NSDSPPH is a major project of the national science and technology infrastructure. It was launched in 2003. Clinical investigators may be interested in clinical data contained in NSDSPPH. The clinical data center is jointly established by Peking Union medical college hospital and Chinese PLA general hospital. After more than 10 years of endeavor, the clinical data center has become more and more sophisticated. Clinical data were collected from hospitals nationwide with strict criteria for data entry. Data are presented in several forms such as raw data, summary data and statistical report. The clinical data are accessible to the public and researchers. Additionally, the center also provides services of statistical analysis and data mining. Numerous scientific papers have been published using the database. Some examples include the correlation between waist circumference and respiratory function in teenagers (1), epidemiological study on coexisting prehypertension and prediabetes in northern China (2), and the Association between γ-glutamyltransferase and prehypertension (3).

Temporal Update Dynamics Under Blind Sampling

Network applications commonly maintain local copies of remote data sources in order to provide caching, indexing, and data-mining services to their clients. Modeling performance of these systems and predicting future updates usually requires knowledge of the inter-update distribution at the source, which can only be estimated through blind sampling—periodic downloads and comparison against previous copies. In this paper, we first introduce a stochastic modeling framework for this problem, where updates and sampling follow independent point processes. We then show that all previous approaches are biased unless the observation rate tends to infinity or the update process is Poisson. To overcome these issues, we propose four new algorithms that achieve various levels of consistency, which depend on the amount of temporal information revealed by the source and capabilities of the download process.

System architecture of coastal remote sensing data mining and services based on cloud computing

Coastal remote sensing images have huge data which has features of the volume, variety, complexity and specialisation. The problem is how to effectively carry out data integration, fast extract and data mining of knowledge and information from these massive remote sensing data as it is far behind the requirements of coastal professional applications. In this paper, based on data mining, remote sensing theory, space information and cloud computing technology towards the goal of coastal zone remote sensing data integration and data mining service system, the meta-data model, data storage model, data mining framework, web service framework model, etc. are provided and designed. Finally, a prototype system of remote sensing data mining services in the cloud computing environment is designed and developed using system integration, and is demonstrated and verified by professional applications. It is valuable to serve the development of the coastal zone, monitoring, planning and integrated management and other fields.

Research of Data-Aiming Mining Algorithm in Cloud Environment

Cloud computing contains a huge amount of data, which are featured as being widely distributed, heterogeneous, and dynamic. Thus, aiming at how to mine useful parts in these information, this paper proposes an Apriori algorithm based on cloud computing and introduces cost-sensitive learning and non-filter matrix to find k frequency set and uses the method of generating association rules to improve effectiveness of data mining. Simulation experiments show that mining algorithm in this paper is highly effective and suitable for data mining in the context of cloud computing. With the emergence of the concept cloud computing, more and more information is shared and spread through the Internet. How can carry out data mining in cloud computing became an important means of access to information (1). Literature (2) data mining solutions in the cloud computing environment is proposed, through the cloud computing and cloud computing services, describes the problem solving mechanism for data mining services. Literature (3) proposed a data processing method based on cloud computing, based on mining user browsing preference path. Experiments show that the mining algorithm for large amount of data logs, accuracy and efficiency than ordinary users preferred path mining algorithm based on frequency increased. Literature (4) proposed a new framework for data mining in the field of cloud platforms and cloud computing environments are described mechanism of data mining in the field of service-oriented. Literature (5) is a method of data mining based on cloud computing technology: large data sets and mining tasks on multiple computers in parallel, experiments show that use cloud computing technology to handle large data sets in a cluster, you can significantly improve the efficiency of data mining. Literature (6) for cloud computing resources prediction model based on data mining technology, results showed that this model not only improves the prediction accuracy of cloud computing resources, and reduce the complexity of modeling, improves the efficiency of models, to cloud computing resources provides a new way of building models. Literature (7) proposed data mining services view, cloud services task force has analyzed data mining advantages and challenges, data mining and prediction of cloud point data mining development trend of cloud services, providing data mining services framework for cloud services. This paper proposes an Apriori algorithm based on cloud computing and introduces cost-sensitive learning and non-filter matrix to find k

Design and Adaptation for Internet News Data Extraction Middleware(INDEM) System

In this paper, we propose the INDEM(Internet News Data Extraction Middleware) system for the removal of the unnecessary data in internet news. Although data on the internet can be used in various fields such as source of data of IR(Information Retrieval), Data mining and knowledge information service, it contains a lot of unnecessary information. The removal of the unnecessary data is a problem to be solved prior to the study of the knowledge-based information service that is based on the data of the web page. The INDEM system parses html and explores the XPath, and it is to perform the analysis. The user simply utilize INDEM by implementing an abstract class that provides INDEM, and can obtain the analysis information. INDEM System through this process delivers the analysis information including the main contents of news site to the users. In this paper, the INDEM system was adapted in a stand-alone and web service system and it was evaluated on the basis of 16 news site. As a result, performance of the INDEM system is affected in html source data size and complexity of used html grammar than the main news data size.

Privacy-preserving data mashup model for trading person-specific information

Business enterprises adopt cloud integration services to improve collaboration with their trading partners and to deliver quality data mining services. Data-as-a-Service (DaaS) mashup allows multiple enterprises to integrate their data upon the demand of consumers. Business enterprises face challenges not only to protect private data over the cloud but also to legally adhere to privacy compliance rules when trading person-specific data. They need an effective privacy-preserving business model to deal with the challenges in emerging markets. We propose a model that allows the collaboration of multiple enterprises for integrating their data and derives the contribution of each data provider by valuating the incorporated cost factors. This model serves as a guide for business decision-making, such as estimating the potential risk and finding the optimal value for publishing mashup data. Experiments on real-life data demonstrate that our approach can identify the optimal value in data mashup for different privacy models, including K-anonymity, LKC-privacy, and ∊-differential privacy, with various anonymization algorithms and privacy parameters.

RATIONALE FOR DEVELOPING A UNIVERSAL PLATFORM OF DATA MINING USING SCALABLE COMPUTING RESOURCES FOR HEALTH MANAGEMENT IN THE NAVY

The article explains the issues of implementation, upgrade and support of algorithms for analysis of large data sets, which are hidden from the end user. It is shown that, to create a universal platform of data mining using scalable computing resources for health management in the Navy requires the development of information-analytical core of intelligent system of high-performance analysis of large amounts of data that will successfully work as a developer of services for the creation of the health management system in the Navy (providing the environment and processing power for debugging and research of parallel and distributed versions of the algorithm, as well as a framework and a development tool for local development and debugging of algorithms) and analysts (providing services of data mining by various methods, data processing (conversion, cleaning, etc. etc.) to «unlimited» computing resources.

Refined Clustering of Software Components by Using K-Mean and Neural Network

Data Mining is extraction of relevant information about data set. A data-warehouse is a location where information is stored. There are various services of data mining, clustering is one of them. Clustering is an effort to group similar data onto single cluster. In this paper we propose and implement k-mean and neural network for clustering same components in single cluster. Clustering reduces the search space by grouping similar test cases together according to the requirements and, hence minimizing the search time, for the retrieval of the test cases, resulting in reduced time complexity. In this research paper we proposed approach for re-usability of test cases by unsupervised approach and supervised approach. In unsupervised learning we proposed k-mean and in supervised learning neural network. We have designed the algorithm for requirement and test case document clustering according to its tf-idf vector space and the output is set of highly cohesive pattern groups.

CrossRef text and data mining services

The discipline of text and data mining (TDM) is growing in visibility in the scholarly research community. In May 2014, CrossRef launched CrossRef Text and Data Mining Services, which aims to alleviate the issues that text mining researchers encounter when trying to collect the corpus of content that they want to mine from the different publishers who have produced it. This article will discuss what the CrossRef service does, the problem that it is trying to solve and the current status of the rollout of the service across CrossRef member publishers.

ADME SARfari: comparative genomics of drug metabolizing systems.

Motivation: ADME SARfari is a freely available web resource that enables comparative analyses of drug-disposition genes. It does so by integrating a number of publicly available data sources, which have subsequently been used to build data mining services, predictive tools and visualizations for drug metabolism researchers. The data include the interactions of small molecules with ADME (absorption, distribution, metabolism and excretion) proteins responsible for the metabolism and transport of molecules; available pharmacokinetic (PK) data; protein sequences of ADME-related molecular targets for pre-clinical model species and human; alignments of the orthologues including information on known SNPs (Single Nucleotide Polymorphism) and information on the tissue distribution of these proteins. In addition, in silico models have been developed, which enable users to predict which ADME relevant protein targets a novel compound is likely to interact with.Availability and implementation: https://www.ebi.ac.uk/chembl/admesarfariContact: jpo@ebi.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

Research on the Era Environment of Data Mining

Modern society has entered an era of information technology, computer communication and network technology is changing the entire human and social, blowing a lot of information to bring convenience, it also brings a lot of new questions, the first is information overload , it is difficult to digest: the second is difficult to identify true and false information; third is difficult to ensure information security; fourth is the information in the form of inconsistent, difficult to handle, "Faced with these challenges, data mining techniques have emerged, and have a higher frame model security and privacy protection according distributed heterogeneous environments of data mining features, integrated use of new technologies SOA, ontology, WEB services, follow the user-centric concept, data mining system framework proposed an open service oriented. binding domain knowledge of data mining applications, follow the user-centered design philosophy presented to the user for data mining center of the body, on the one hand based on data mining and mining objects to organize data mining algorithms, on the other hand provide effective data mining application solutions for the user based on the application domain knowledge to help different areas of multi-level users to easily select data mining services.

Cloud Platform Based on Mobile Internet Service Opportunistic Drive and Application Aware Data Mining

Because the static cloud platform cannot satisfy the diversity of mobile Internet service and inefficient data mining problems, we presented a reliable and efficient data mining cloud platform construction scheme based on the mobile Internet service opportunistic driving and application perception. In this scheme, first of all data selection mechanism was established based on mobile Internet service opportunistic drive. Secondly, through the cloud platform different cloud and channel aware, nonlinear mapping from the service to a data set of proposed perceptual model is applied. Finally, on the basis of the driving characteristics and extraction of perceptual features, the cloud platform would be constructed through the service opportunities of mobile Internet applications, which could provide robust and efficient data mining services. The experimental results show that the proposed mechanism, compared to the cloud platform based on distributed data mining, has obvious advantages in system running time, memory usage, and data clustering required time, as well as average clustering quality.

An Overlay-Based Data Mining Architecture Tolerant to Physical Network Disruptions

Management scheme for highly scalable big data mining has not been well studied in spite of the fact that big data mining provides many valuable and important information for us. An overlay-based parallel data mining architecture, which executes fully distributed data management and processing by employing the overlay network, can achieve high scalability. However, the overlay-based parallel mining architecture is not capable of providing data mining services in case of the physical network disruption that is caused by router/communication line breakdowns because numerous nodes are removed from the overlay network. To cope with this issue, this paper proposes an overlay network construction scheme based on node location in physical network, and a distributed task allocation scheme using overlay network technology. The numerical analysis indicates that the proposed schemes considerably outperform the conventional schemes in terms of service availability against physical network disruption.

Toward integrating overlay and physical networks for robust parallel processing architecture

Recently, much research attention is being paid toward the wide-area parallel processing architectures whereby all the network devices in the entire network execute the processing function in order to shorten the completion time of big data mining tasks. Despite some advancement, such architectures still suffer from physical network failures, which lead to critical service unavailability. As a remedy to this problem, an overlay-based parallel processing architecture, where nodes manage each other by employing the overlay network, can achieve higher service availability against small-scale failures. However, the overlay-based parallel processing architecture is not capable of providing data mining services in case large-scale network failures disrupt the overlay network. To deal with this issue, our article introduces a design methodology of an overlay-based parallel processing architecture based on integration of overlay and physical networks. Additionally, we introduce basic principles based on the design methodology. Through numerical calculation, we evaluate the effectiveness of an integration approach on the performance of parallel data processing in terms of higher service availability against physical network failures while minimizing traffic load.

Using semantic types to formalize and augment complex argumentative discourses

Argumentation support systems currently on the Web have yet to deliver their full potential to teams striving for informed sense and decision making. Today's widely used systems such as discussion forums do not support formalization and are poorly integrated in the environment of multidisciplinary teams that collaborate in data intensive and cognitive complex settings, such as those involving DNA analysis, marketing or drug testing research. Such teams use on a daily basis tools to collect big amounts of required data as well as sophisticated data mining tools to uncover patterns in the collected data. However, these tools do not interoperate with argumentation support systems. In this paper, we present an approach to support collaboration which exploits a range of semantic types to enable formalization of argumentative discourses. Semantic types also enable the integration of argumentation support systems with data mining services to further augment collaboration and decision making in the above teams. An evaluation of the approach shows that the platform enables stakeholders to make better, more informed and quicker decisions, by displaying the aggregated information according to their needs. The overall idea of our approach builds on the exploitation of the synergy between tools supporting machine and human intelligence.

Data mining for CRM: Towards an integrative approach

This paper introduces an approach for integrating numerical, textual and network data into a predictive data-mining model for customer relationship management (CRM). Traditional data-mining services leverage single data types in analytical decision making. This provides significant value to businesses in customer identification and retention. It is expected that enabling the capability to leverage network and device information for use in these decision-making functions will not only provide further value to businesses in traditional CRM dimensions but also result in net benefit to network security.

Ionosphere Waves Service (IWS) – a problem-oriented tool in ionosphere and Space Weather research produced by POPDAT project

In the frame of the FP7 POPDAT project the Ionosphere Waves Service (IWS) has been developed and opened for public access by ionosphere experts. IWS is forming a database, derived from archived ionospheric wave records to assist the ionosphere and Space Weather research, and to answer the following questions: How can the data of earlier ionospheric missions be reprocessed with current algorithms to gain more profitable results? How could the scientific community be provided with a new insight on wave processes that take place in the ionosphere? The answer is a specific and unique data mining service accessing a collection of topical catalogs that characterize a huge number of recorded occurrences of Whistler-like Electromagnetic Wave Phenomena, Atmosphere Gravity Waves, and Traveling Ionosphere Disturbances. IWS online service (http://popdat.cbk.waw.pl) offers end users to query optional set of predefined wave phenomena, their detailed characteristics. These were collected by target specific event detection algorithms in selected satellite records during database buildup phase. Result of performed wave processing thus represents useful information on statistical or comparative investigations of wave types, listed in a detailed catalog of ionospheric wave phenomena. The IWS provides wave event characteristics, extracted by specific software systems from data records of the selected satellite missions. The end-user can access targets by making specific searches and use statistical modules within the service in their field of interest. Therefore the IWS opens a new way in ionosphere and Space Weather research. The scientific applications covered by IWS concern beyond Space Weather also other fields like earthquake precursors, ionosphere climatology, geomagnetic storms, troposphere-ionosphere energy transfer, and trans-ionosphere link perturbations.

Oblique Decision Tree Learning Approaches - A Critical Review

Decision tree classification techniques are currently gaining increasing impact especially in the light of the ongoing growth of data mining services. A central challenge for the decision tree classification is the identification of split rule and correct attributes. In this context, the article aims at presenting the current state of research on different techniques for classification using oblique decision tree. A variation to the traditional approach is the called oblique decision tree or multivariate decision tree, which allows multivariate tests in its non-terminal nodes. Univariate trees can only perform axis-parallel splits, whereas Oblique decision trees can model the decision boundaries that are oblique to attribute axis. The majority of these decision tree induction algorithms performs a top-down growing tree strategy and relay on an impuritybased measure for splitting nodes criteria. In this context, the article aims at presenting the current state of research on different techniques for Oblique Decision Tree classification. For this, the paper analyzes various traditional Multivariate and Oblique Decision Tree algorithms CART, OC1 as well as standard SVM, GDT implementation. General Terms .Data mining ,algorithm for tree building.

Study on Knowledge Services Based on Multi-Media Data Mining

There are a lot of data in the real life showed by the form of multimedia data. But the vast majority of data mining tools are developed for the relational database. Therefore it is necessary to introduce the service in multimedia data mining and multimedia data mining technology to improve service quality and level of knowledge. Multimedia data mining and knowledge services are introduced and multimedia data mining process is given. Meanwhile, multimedia data mining system prototype framework is proposed. In the end multimedia data mining application in the knowledge services is discussed.