Data Mining Services Research Articles

Astrophysical data mining with GPU. A case study: Genetic classification of globular clusters

We present a multi-purpose genetic algorithm, designed and implemented with GPGPU/CUDA parallel computing technology. The model was derived from our CPU serial implementation, named GAME (Genetic Algorithm Model Experiment). It was successfully tested and validated on the detection of candidate Globular Clusters in deep, wide-field, single band HST images. The GPU version of GAME will be made available to the community by integrating it into the web application DAMEWARE (DAta Mining Web Application REsource, http://dame.dsf.unina.it/beta_info.html), a public data mining service specialized on massive astrophysical data. Since genetic algorithms are inherently parallel, the GPGPU computing paradigm leads to a speedup of a factor of 200× in the training phase with respect to the CPU based version.

A Novel Approach Based on Nearest Neighbor Search on Encrypted Databases

Data mining services which permit designers and individuals to store their data to a server, and reduce maintenance cost. The spatial queries does not provide privacy, because the location of the query reveal sensitive information about the query. Only authorized user is allowed to access the query, even though service provider not able to view the query. This paper focuses on adducing a novel k- nearest neighbor search on the encrypted database. It describes the strong location privacy that renders a query identical in data space from any location. Due to communication cost in query processing, existing work fails to hold this search. We include a method that endeavors strong location privacy, by amalgamate Metric Preserving Transformation (MPT). Empirical results reveal that efficacy and performance of the adduced methodology has been increased and as compared to the existing methodologies

A Novel Association Rule Hiding Approach in OLAP Data Cubes

Data mining services require exact input data for their outcomes to be significant, but privacy concerns may influence users to provide fake information. We study here, with respect to mining association rules, whether or not users can be confident to provide correct information by ensuring that the mining process cannot, with any reasonable degree of certainty, breach their privacy. A data warehouse stores current and historical records consolidated from multiple transactional systems. Protecting data warehouses is of rising interest, particularly in view of areas where data are sold in pieces to third parties for data mining studies. In this case, current normal data warehouse security techniques, like data access control, may not be easy to impose and can be in effective. As an alternative, this paper proposes a data perturbation based approach, to provide privacy preserving in association rule mining on data cubes in a data warehouse. In order to conceal association rules and save the utility of transactions in data cubes, we select Genetic Algorithm to find optimum state of modification. In our approach various hiding styles are applied in different multi-objective fitness functions. To cope with the multi-objective functions, Pareto-front ranking strategy has been applied for obtaining the non-dominated solutions front. First objective of these functions is hiding sensitive rules and the second one is keeping the accuracy of transactions in data cube. After sanitization process we test the sanitization performance by evaluation of various criterions. The major feature is that the proposed strategy does not affect the functionality of the On-Line Analytical Processing system. Finally our experimental results show its effectiveness and feasibility.

Analysis on Data Mining Model Objected to Internet of Things

Three data mining models suitable to data processing have been proposed according to some technical challenges on the characteristics data for Internet of Things. The first model is the multilayer data mining model, which consists of data collection, data processing, event processing and data mining service. The second model is distributed data mining model that could be used to solve the problem of data stored in different locations. The third model is grid-based data mining model, which could utilize the potentially unlimited amount of data by using grid. At the same time, the paper learns from “decomposition build theory to solve the data mining problem in the internet of things. Finally, the key issues are discussed about model application.

Programming knowledge discovery workflows in service‐oriented distributed systems

SUMMARYIn several scientific and business domains, very large data repositories are generated. To find interesting and useful information in those repositories, efficient data mining techniques and knowledge discovery processes must be used. The exploitation of data mining techniques in science helps scientists in hypothesis formation and gives them a support on their scientific practices, whereas in industrial processes, data mining can exploit existing data sources as a real value for companies that can take advantage from the knowledge that can be extracted from their large data sources. Data mining tasks are often composed by multiple stages that may be linked to each other to form various execution flows. Moreover, data mining tasks are often distributed because they involve data and tools located over geographically distributed environments. Therefore, it is fundamental to exploit effective paradigms, such as services and workflows, to model data mining tasks that are both multi‐staged and distributed. This paper discusses data mining services and workflows for analyzing scientific data in high‐performance distributed environments such as Grids and Clouds. We discuss how it is possible to define basic and complex services for supporting distributed data mining tasks in Grids. We also present a workflow formalism and a service‐oriented programming framework, named DIS3GNO, for designing and running distributed knowledge discovery processes in the Knowledge Grid system. DIS3GNO supports all the phases of a knowledge discovery process, including composition, execution, and results visualization. After introducing DIS3GNO, some relevant use cases implemented by it and a performance evaluation of the system are discussed.Copyright © 2012 John Wiley & Sons, Ltd.

Semantic Subgroup Discovery Systems and Workflows in the SDM-Toolkit

This paper addresses semantic data mining, a new data mining paradigm in which ontologies are exploited in the process of data mining and knowledge discovery. This paradigm is introduced together with new semantic subgroup discovery systems SDM-search for enriched gene sets (SEGS) and SDM-Aleph. These systems are made publicly available in the new SDM-Toolkit for semantic data mining. The toolkit is implemented in the Orange4WS data mining platform that supports knowledge discovery workflow construction from local and distributed data mining services. On the basis of the experimental evaluation of semantic subgroup discovery systems on two publicly available biomedical datasets, the paper results in a thorough quantitative and qualitative evaluation of SDM-SEGS and SDM-Aleph and their comparison with SEGS, a system for enriched gene set discovery from microarray data.

A service oriented architecture to provide data mining services for non-expert data miners

In today's competitive market, companies need to use discovery knowledge techniques to make better, more informed decisions. But these techniques are out of the reach of most users as the knowledge discovery process requires an incredible amount of expertise. Additionally, business intelligence vendors are moving their systems to the cloud in order to provide services which offer companies cost-savings, better performance and faster access to new applications. This work joins both facets. It describes a data mining service addressed to non-expert data miners which can be delivered as Software-as-a-Service. Its main advantage is that by simply indicating where the data file is, the service itself is able to perform all the process.

Physicists Get INSPIREd: INSPIRE Project and Grid Applications

INSPIRE is the new high-energy physics scientific information system developed by CERN, DESY, Fermilab and SLAC. INSPIRE combines the curated and trusted contents of SPIRES database with Invenio digital library technology. INSPIRE contains the entire HEP literature with about one million records and in addition to becoming the reference HEP scientific information platform, it aims to provide new kinds of data mining services and metrics to assess the impact of articles and authors. Grid and cloud computing provide new opportunities to offer better services in areas that require large CPU and storage resources including document Optical Character Recognition (OCR) processing, full-text indexing of articles and improved metrics. D4Science-II is a European project that develops and operates an e-Infrastructure supporting Virtual Research Environments (VREs). It develops an enabling technology (gCube) which implements a mechanism for facilitating the interoperation of its e-Infrastructure with other autonomously running data e-Infrastructures. As a result, this creates the core of an e-Infrastructure ecosystem. INSPIRE is one of the e-Infrastructures participating in D4Science-II project. In the context of the D4Science-II project, the INSPIRE e-Infrastructure makes available some of its resources and services to other members of the resulting ecosystem. Moreover, it benefits from the ecosystem via a dedicated Virtual Organization giving access to an array of resources ranging from computing and storage resources of grid infrastructures to data and services.

SegMine workflows for semantic microarray data analysis in Orange4WS

BackgroundIn experimental data analysis, bioinformatics researchers increasingly rely on tools that enable the composition and reuse of scientific workflows. The utility of current bioinformatics workflow environments can be significantly increased by offering advanced data mining services as workflow components. Such services can support, for instance, knowledge discovery from diverse distributed data and knowledge sources (such as GO, KEGG, PubMed, and experimental databases). Specifically, cutting-edge data analysis approaches, such as semantic data mining, link discovery, and visualization, have not yet been made available to researchers investigating complex biological datasets.ResultsWe present a new methodology, SegMine, for semantic analysis of microarray data by exploiting general biological knowledge, and a new workflow environment, Orange4WS, with integrated support for web services in which the SegMine methodology is implemented. The SegMine methodology consists of two main steps. First, the semantic subgroup discovery algorithm is used to construct elaborate rules that identify enriched gene sets. Then, a link discovery service is used for the creation and visualization of new biological hypotheses. The utility of SegMine, implemented as a set of workflows in Orange4WS, is demonstrated in two microarray data analysis applications. In the analysis of senescence in human stem cells, the use of SegMine resulted in three novel research hypotheses that could improve understanding of the underlying mechanisms of senescence and identification of candidate marker genes.ConclusionsCompared to the available data analysis systems, SegMine offers improved hypothesis generation and data interpretation for bioinformatics in an easy-to-use integrated workflow environment.

Distributed data mining patterns and services: an architecture and experiments

SUMMARYDistributed data mining implements techniques for analyzing data on distributed computing systems by exploiting data distribution and parallel algorithms. The grid is a computing infrastructure for implementing distributed high‐performance applications and solving complex problems, offering effective support to the implementation and use of data mining and knowledge discovery systems. The Web Services Resource Framework has become the standard for the implementation of grid services and applications, and it can be exploited for developing high‐level services for distributed data mining applications. This paper describes how distributed data mining patterns, such as collective learning, ensemble learning, and meta‐learning models, can be implemented as Web Services Resource Framework mining services by exploiting the grid infrastructure. The goal of this work was to design a distributed architectural model that can be exploited for different distributed mining patterns deployed as grid services for the analysis of dispersed data sources. In order to validate such an approach, we presented also the implementation of two clustering algorithms on the developed architecture. In particular, the distributed k‐means and distributed expectation maximization were exploited as pilot examples to show the suitability of the implemented service‐oriented framework. An extensive evaluation of its performance was provided. Copyright © 2011 John Wiley & Sons, Ltd.

Orange4WS Environment for Service-Oriented Data Mining

Novel data-mining tasks in e-science involve mining of distributed, highly heterogeneous data and knowledge sources. However, standard data mining platforms, such as Weka and Orange, involve only their own data mining algorithms in the process of knowledge discovery from local data sources. In contrast, next generation data mining technologies should enable processing of distributed data sources, the use of data mining algorithms implemented as web services, as well as the use of formal descriptions of data sources and knowledge discovery tools in the form of ontologies, enabling automated composition of complex knowledge discovery workflows for a given data mining task. This paper proposes a novel Service-oriented Knowledge Discovery framework and its implementation in a service-oriented data mining environment Orange4WS (Orange for Web Services), based on the existing Orange data mining toolbox and its visual programming environment, which enables manual composition of data mining workflows. The new service-oriented data mining environment Orange4WS includes the following new features: simple use of web services as remote components that can be included into a data mining workflow; simple incorporation of relational data mining algorithms; a knowledge discovery ontology to describe workflow components (data, knowledge and data mining services) in an abstract and machine-interpretable way, and its use by a planner that enables automated composition of data mining workflows. These new features are showcased in three real-world scenarios.

Distortion Based Algorithms for Privacy Preserving Frequent Item Set Mining

Data mining services require accurate input data for their results to be meaningful, but privacy concerns may influence users to provide spurious information. In order to preserve the privacy of the client in data mining process, a variety of techniques based on random perturbation of data records have been proposed recently. We focus on an improved distortion process that tries to enhance the accuracy by selectively modifying the list of items. The normal distortion procedure does not provide the flexibility of tuning the probability parameters for balancing privacy and accuracy parameters, and each item's presence/absence is modified with an equal probability. In improved distortion technique, frequent one item-sets, and nonfrequent one item-sets are modified with a different probabilities controlled by two probability parameters fp, nfp respectively. The owner of the data has a flexibility to tune these two probability parameters (fp and nfp) based on his/her requirement for privacy and accuracy. The experiments conducted on real time datasets confirmed that there is a significant increase in the accuracy at a very marginal cost in privacy.

Interconnection among academic journal websites: multilateral versus bilateral interconnection

Electronic academic journal websites provide text and data mining (and linking) services. Fully realizing the benefit of these services requires interconnection among websites. We perform a comparison between multilateral interconnection through an open platform and bilateral interconnection, and find that publishers are fully interconnected in the former regime whereas they are often partially interconnected in the latter regime for exclusion or differentiation motives. If partial interconnection arises for differentiation motives, exclusion of a small publisher(s) occurs more often in the former than in the latter. In the case of multilateral interconnection, an open platform generates lower welfare than a for‐profit platform.

A Novel RSRM Algorithm for Mining services for Better Enhancement in Small HandHeld Devices

This paper presents a case study on data mining services able to support decision makers in strategic planning for the enhancement of small handheld devices. The application provides e-Knowledge services for the analysis of territorial dynamics by processing and modeling huge amount of data, in order to discover rules and patterns in a distributed and heterogeneous content environment. For the analysis of structured data, the application covers the whole Knowledge Discovery process. The purpose of the paper is to show how to implement existing techniques in a flexible architecture for providing new added value services. Finally in our paper, we proposed a RSRM( Read Subset Removal Miner) algorithm for mining on mobile devices.

WS-DAI-DM: An Interface Specification for Data Mining in Grid Environments

Providing the appropriate access means for data mining services in Grid Environment is principal for com- bination of Grid and data mining. The transition from cen- tralized data mining process as they are in traditional tools to Grid-compliant and Grid-based data mining services that can coordinate with each other is important to extract useful and potential knowledge/patterns from distributed data resources. But, data mining process has not been integrated with the current Grid architecture, and there are no related specifications or standards available within the Grid com- munity for handling it. The work discusses the interface specification WS-DAI-DM and related issues of providing the consistent web service interfaces to mine meaningful and hidden information/patterns from distributed data re- sources in Grid environments. WS-DAI-DM can facilitate using data mining tools and developing data mining applica- tions. The proposed specification idea is partially imple- mented in the feasibility study and we give an application scenario about how users can access data mining services in Grid environments conveniently via the proposed mecha- nism.

Why privacy discussions about pervasive online customer profiling should focus on the expanding roles of third-parties

Network advertising associations, consumer databases, data mining services and advertising exchanges play important roles in the online behavioural advertising industry. The participation of such third-party businesses in consumer profiling to generate targeted direct marketing communications raises significant consumer privacy and data protection concerns. This article analyses the regulatory frameworks of the European Union and the USA and legal developments on this topic to assess how well consumers' privacy concerns are being addressed. It then makes suggestions for regulatory reform specific to third-party involvement in the behavioural advertising industry aimed at enhancing consumers' privacy and data protection.

Exploiting Need of Heterogeneous Data Mining Environment Based on Mobile Computing Environments

the concept of Data Mining services is not alone sufficient. Data mining services play an important role in the field of Communication industry. Data mining is also called knowledge discovery in several database including mobile databases and for heterogeneous environment. In this paper, we discuss and analyze the consumptive behavior based on data mining technology. We discuss and analyze different aspects of data mining techniques and their behavior in mobile devices. We also analyze the better method or rule of data mining services which is more suitable for mobile devices. In this paper, we survey several aspects of open service framework based on grid structure which provides the heterogeneous environment for data mining on mobile computing environments. discovering frequent user's behavior patterns in the mobile agent system have been studied extensively in recent years. The key feature in most of these algorithms is that they use a dataset and frequent Item-Sets visited by the customers. In this case, some problems occur because they do not consider that mobile user's behavior patterns are dynamically variable as time passes. In this paper we discuss some of the data mining service which are use in different areas and then apply those services to mobile devices and then apply those DMS services in mobile computing and exploiting the need of DMS in mobile computing environments using CLDC and MIDP components. The Connected, Limited Device Configuration (CLDC) and the Mobile Information Device Profile (MIDP) have emerged as J2ME standards for mobile phone applications development which are used with DMS services. The role of CLDC and MIDP component is to apply Data Mining Services in mobile. Data Mining Services are useful in several sectors including Mobile, WWW, HealthCare scenario etc. We discuss several aspects step-by-step in this paper and analyze those aspects and approaches sequentially. Discuss their advantages and disadvantages and conclude with new concept.

A Survey on Mining services for Better Enhancement in Small HandHeld Devices

This paper presents a case study on data mining services able to support decision makers in strategic planning for the enhancement of small handheld devices. The application provides e-Knowledge services for the analysis of territorial dynamics by processing and modeling huge amount of data, in order to discover rules and patterns in a distributed and heterogeneous content environment. For the analysis of structured data, the application covers the whole Knowledge Discovery process. The purpose of the paper is to show how to implement existing techniques in a flexible architecture for providing new added value services. Finally in our paper, a case study of different data mining task is thrive under different category like in WWW, Mobile environment, PDA Devices, Web log techniques etc. We also use MIDP (Mobile Information device Profile) and CLDC (Connected Limited Device Configuration) of J2ME.

A Novel CSUA Based Data Mining Approach for Mobile Computing Environments

mining services play an important role in the field of Communication industry. Data mining is also called knowledge discovery in several database including mobile databases. In this paper, we discuss and analyze the consumptive behavior based on data mining technology. We discuss and analyze different aspects of data mining techniques and their behavior in mobile devices. We also analyze the better method or rule of data mining services which is more suitable for mobile devices. In this paper, we propose a novel CSUA (Create, Select, Update and Alter) Based data mining approach for mobile computing environments. In CSUA approach, we first propose to create a data set according to the requirement and need then select the data set, then update the data set according to the customers and finally reconstruct the structure of the dataset. It is achieved by CLDC and MIDP component of J2ME.

JsPhyloSVG: A Javascript Library for Visualizing Interactive and Vector-Based Phylogenetic Trees on the Web

BackgroundMany software packages have been developed to address the need for generating phylogenetic trees intended for print. With an increased use of the web to disseminate scientific literature, there is a need for phylogenetic trees to be viewable across many types of devices and feature some of the interactive elements that are integral to the browsing experience. We propose a novel approach for publishing interactive phylogenetic trees.Methods/Principal FindingsWe present a javascript library, jsPhyloSVG, which facilitates constructing interactive phylogenetic trees from raw Newick or phyloXML formats directly within the browser in Scalable Vector Graphics (SVG) format. It is designed to work across all major browsers and renders an alternative format for those browsers that do not support SVG. The library provides tools for building rectangular and circular phylograms with integrated charting. Interactive features may be integrated and made to respond to events such as clicks on any element of the tree, including labels.Conclusions/SignificancejsPhyloSVG is an open-source solution for rendering dynamic phylogenetic trees. It is capable of generating complex and interactive phylogenetic trees across all major browsers without the need for plugins. It is novel in supporting the ability to interpret the tree inference formats directly, exposing the underlying markup to data-mining services. The library source code, extensive documentation and live examples are freely accessible at www.jsphylosvg.com.