Distributed Data Mining Research Articles

Distributed data mining services leveraging WSRF

The continuous increase of data volumes available from many sources raises new challenges for their effective understanding. Knowledge discovery in large data repositories involves processes and activities that are computationally intensive, collaborative, and distributed in nature. The Grid is a profitable infrastructure that can be effectively exploited for handling distributed data mining and knowledge discovery. To achieve this goal, advanced software tools and services are needed to support the development of KDD applications. The Knowledge Grid is a high-level framework providing Grid-based knowledge discovery tools and services. Such services allow users to create and manage complex knowledge discovery applications that integrate data sources and data mining tools provided as distributed services on the Grid. All of these services are currently being re-designed and re-implemented as WSRF-compliant Grid Services. This paper highlights design aspects and implementation choices involved in such a process.

Distributed feature extraction in a p2p setting — a case study

Finding the right data representation is essential for virtually every data mining application. In this work we describe an approach to collaborative feature extraction, selection and aggregation in distributed, loosely coupled domains. In contrast to other work in the field of distributed data mining, we focus on scenarios in which a large number of loosely coupled nodes apply data mining to different, usually very small and overlapping, subsets of the entire data space. The aim is not to find a global concept to cover all data, but to learn a set of local concepts. Our prototypical application is a distributed media organization platform, called Nemoz, that assists users in maintaining their media collections. We propose two models for collaborative feature extraction, selection and aggregation for supervised data mining. One is based on a centralized p2p architecture, and the other on a fully distributed p2p architecture. We compare both models on a real world data set and discuss their advantages and problems.

Distributed data mining in grid computing environments

The computing-intensive data mining for inherently Internet-wide distributed data, referred to as Distributed Data Mining (DDM), calls for the support of a powerful Grid with an effective scheduling framework. DDM often shares the computing paradigm of local processing and global synthesizing. It involves every phase of Data Mining (DM) processes, which makes the workflow of DDM very complex and can be modelled only by a Directed Acyclic Graph (DAG) with multiple data entries. Motivated by the need for a practical solution of the Grid scheduling problem for the DDM workflow, this paper proposes a novel two-phase scheduling framework, including External Scheduling and Internal Scheduling, on a two-level Grid architecture (InterGrid, IntraGrid). Currently a DM IntraGrid, named DMGCE (Data Mining Grid Computing Environment), has been developed with a dynamic scheduling framework for competitive DAGs in a heterogeneous computing environment. This system is implemented in an established Multi-Agent System (MAS) environment, in which the reuse of existing DM algorithms is achieved by encapsulating them into agents. Practical classification problems from oil well logging analysis are used to measure the system performance. The detailed experiment procedure and result analysis are also discussed in this paper.

Random projection-based multiplicative data perturbation for privacy preserving distributed data mining

This paper explores the possibility of using multiplicative random projection matrices for privacy preserving distributed data mining. It specifically considers the problem of computing statistical aggregates like the inner product matrix, correlation coefficient matrix, and Euclidean distance matrix from distributed privacy sensitive data possibly owned by multiple parties. This class of problems is directly related to many other data-mining problems such as clustering, principal component analysis, and classification. This paper makes primary contributions on two different grounds. First, it explores independent component analysis as a possible tool for breaching privacy in deterministic multiplicative perturbation-based models such as random orthogonal transformation and random rotation. Then, it proposes an approximate random projection-based technique to improve the level of privacy protection while still preserving certain statistical characteristics of the data. The paper presents extensive theoretical analysis and experimental results. Experiments demonstrate that the proposed technique is effective and can be successfully used for different types of privacy-preserving data mining applications.

Simple Available Bandwidth Utilization Library for High-Speed Wide Area Networks

The growth of high-speed wide area networks (WANs) has enabled the emergence of new classes of data intensive, wide area computing applications, such as the remote analysis and exploration of data and distributed data mining. The de facto standard for reliable data transfers is the Transmission Control Protocol (TCP). Despite improvements in TCP over the years (to reduce overhead and achieve higher throughput), it is still a common experience for the end-to-end data rates of wide area data mining and data analysis applications to be inefficient. This paper introduces a library called SABUL (Simple Available Bandwidth Utilization Library) which merges features of UDP and TCP to produce a light-weight protocol with flow control, rate control and reliable transmission mechanisms that is designed for data intensive applications over wide area high performance networks. In this paper, we describe the library and give experimental evidence of its effectiveness.

Distributed data mining and agents

Multi-agent systems (MAS) offer an architecture for distributed problem solving. Distributed data mining (DDM) algorithms focus on one class of such distributed problem solving tasks—analysis and modeling of distributed data. This paper offers a perspective on DDM algorithms in the context of multi-agents systems. It discusses broadly the connection between DDM and MAS. It provides a high-level survey of DDM, then focuses on distributed clustering algorithms and some potential applications in multi-agent-based problem solving scenarios. It reviews algorithms for distributed clustering, including privacy-preserving ones. It describes challenges for clustering in sensor-network environments, potential shortcomings of the current algorithms, and future work accordingly. It also discusses confidentiality (privacy preservation) and presents a new algorithm for privacy-preserving density-based clustering.

Distributed higher order association rule mining using information extracted from textual data

The burgconing amount of textual data in distributed sources combined with the obstacles involved in creating and maintaining central repositories motivates the need for effective distributed information extraction and mining techniques. Recently, as the need to mine patterns across distributed databases has grown, Distributed Association Rule Mining (D-ARM) algorithms have been developed. These algorithms, however, assume that the databases are either horizontally or vertically distributed. In the special case of databases populated from information extracted from textual data, existing D-ARM algorithms cannot discover rules based on higher-order associations between items in distributed textual documents that are neither vertically nor horizontally distributed, but rather a hybrid of the two. In this article we present D-HOTM, a framework for Distributed Higher Order Text Mining. D-HOTM is a hybrid approach that combines information extraction and distributed data mining. We employ a novel information extraction technique to extract meaningful entities from unstructured text in a distributed environment. The information extracted is stored in local databases and a mapping function is applied to identify globally unique keys. Based on the extracted information, a novel distributed association rule mining algorithm is applied to discover higher-order associations between items (i.e., entities) in records fragmented across the distributed databases using the keys. Unlike existing algorithms, D-HOTM requires neither knowledge of a global schema nor that the distribution of data be horizontal or vertical. Evaluation methods are proposed to incorporate the performance of the mapping function into the traditional support metric used in ARM evaluation. An example application of the algorithm on distributed law enforcement data demonstrates the relevance of D-HOTM in the fight against terrorism.

Parallel and distributed computing for cybersecurity

Parallel and distributed data mining offer great promise for addressing cybersecurity. The Minnesota Intrusion Detection System can detect sophisticated cyberattacks on large-scale networks that are hard to detect using signature-based systems.

Internet-Based Computing

Internet-Based Computing Dear Readers, It is our pleasure to present to you a long-awaited special issue of the SCPE journal. Since the issue is finally published in the rejuvenated SCPE, we will not delve into the reasons for the delay. Instead, we present to you 8 extended papers that were originally presented during the First International Symposium on Parallel and Distributed Computing that took place in Iasi, Romania, in August, 2001. Out of the collection of very good papers we have selected these that dealt with Internet-oriented computing. The first three pages deal with peer-to-peer and meta computing: W. Hoschek Enabling Rich Service and Resource Discovery with Database for Dynamic Distributed Content, K. Power, J. P. Morrison Ad Hoc Metacomputig With Compeer and J. P. Morrison, P. D. Healy Implementing a Condensed Graphs Based Metacomputer with XML. The next three papers deal with various issues in performance analysis: G. Da Costa, R. Olivier Impact of RealisticWorkload in Peer-to-Peer Systems a Case Study: Freenet, E. Caron, F. Desprez, F. Suter Parallel Extension of a Dynamic Performance Forecasting Tool, and R. Harakaly, P. Primet, F. Bonnassieux, B. Gaidioz Probes Coordination Protocol for Network Performance Measurement in GRID Computing Environment. The remaining three papers are: D. Weyns, E. Eddy, P. Verbaeten Serialization of Distributed Threads in Java, V. Fiolet, B. Toursel Distributed Data Mining, and W. Gawinecki, M. Gordon, P. Kaczmarek, M. Paprzycki The Problem of Agent-Client Communication on the Internet. W believe that even though time has passed since original preparation, the quality of the papers remains high and they are definitely worthy your attention. Dan Grigoras John Morrison Marcin Paprzycki

A hybrid model for improving response time in distributed data mining

This paper presents a hybrid distributed data mining (DDM) model for optimization of response time. The model combines a mobile agent approach with client server strategies to reduce the overall response time. The hybrid model proposes and develops accurate a priori estimates of the computation and communication components of response time as the costing strategy to support optimization. Experimental evaluation of the hybrid model is presented.

Best basis-based intelligent machine fault diagnosis

The wavelet packet transform decomposes a signal into a set of bases for time–frequency analysis. This decomposition creates an opportunity for implementing distributed data mining where features are extracted from different wavelet packet bases and served as feature vectors for applications. This paper presents a novel approach for integrated machine fault diagnosis based on localised wavelet packet bases of vibration signals. The best basis is firstly determined according to its classification capability. Data mining is then applied to extract features and local decisions are drawn using Bayesian inference. A final conclusion is reached using a weighted average method in data fusion. A case study on rolling element bearing diagnosis shows that this approach can greatly improve the accuracy of diagnosis.

Communication-Efficient Distributed Mining of Association Rules

Mining for associations between items in large transactional databases is a central problem in the field of knowledge discovery. When the database is partitioned among several share-nothing machines, the problem can be addressed using distributed data mining algorithms. One such algorithm, called CD, was proposed by Agrawal and Shafer and was later enhanced by the FDM algorithm of Cheung, Han et al. The main problem with these algorithms is that they do not scale well with the number of partitions. They are thus impractical for use in modern distributed environments such as peer-to-peer systems, in which hundreds or thousands of computers may interact.In this paper we present a set of new algorithms that solve the Distributed Association Rule Mining problem using far less communication. In addition to being very efficient, the new algorithms are also extremely robust. Unlike existing algorithms, they continue to be efficient even when the data is skewed or the partition sizes are imbalanced. We present both experimental and theoretical results concerning the behavior of these algorithms and explain how they can be implemented in different settings.

Metadata for Managing Grid Resources in Data Mining Applications

The Grid is an infrastructure for resource sharing and coordinated use of those resources in dynamic heterogeneous distributed environments. The effective use of a Grid requires the definition of metadata for managing the heterogeneity of involved resources that include computers, data, network facilities, and software tools provided by different organizations. Metadata management becomes a key issue when complex applications, such as data-intensive simulations and data mining applications, are executed on a Grid. This paper discusses metadata models for heterogeneous resource management in Grid-based data mining applications. In particular, it discusses how resources are represented and managed in the Knowledge Grid, a framework for Grid-enabled distributed data mining. The paper illustrates how XML-based metadata is used to describe data mining tools, data sources, mining models, and execution plans, and how metadata is used for the design and execution of distributed knowledge discovery applications on Grids.

Experimental studies using photonic data services at IGrid 2002

We describe an architecture for remote and distributed data intensive applications that integrates optical path services, network protocol services for high performance data transport, and data services for remote data analysis and distributed data mining. We also present experimental evidence using geoscience data that this architecture scales to long haul, high performance networks.

Ontology-based distributed autonomous knowledge systems

Traditional query processing usually requires that users fully understand the database structure and content to issue a query. Due to the complexity of the database applications and the variety of user needs, the so-called global queries are introduced which traditional query answering systems cannot handle. Query posed to a database D is global if minimum one of its attributes is missing in D while it occurs in other databases. Definitions of a missing attribute in D can be extracted from other databases and shared with D. To handle semantics inconsistencies between the same attributes used at different sites, task ontologies are used as a communication bridge between them. These inconsistencies can be caused either by different granularity levels or by different interpretations of the same attribute. As the final outcome of this research, a rough query answering system based on distributed data mining is presented.

HANDLING SEMANTIC INCONSISTENCIES IN QUERY ANSWERING BASED ON DISTRIBUTED KNOWLEDGE MINING

Traditional query processing provides exact answers to queries. It usually requires that users fully understand the database structure and content to issue a query. Due to the complexity of the database applications, the so-called global queries can be posed which traditional query answering systems cannot handle. Query posed to a database D is global if a minimum of its attributes is missing in D while it occurs in other databases. In this paper a query answering system based on distributed data mining is presented to rectify these problems. Task ontologies are used as a tool to handle semantic inconsistencies between sites.

Tools for privacy preserving distributed data mining

Privacy preserving mining of distributed data has numerous applications. Each application poses different constraints: What is meant by privacy, what are the desired results, how is the data distributed, what are the constraints on collaboration and cooperative computing, etc. We suggest that the solution to this is a toolkit of components that can be combined for specific privacy-preserving data mining applications. This paper presents some components of such a toolkit, and shows how they can be used to solve several privacy-preserving data mining problems.

A data mining toolset for distributed high- performance platforms

Today a large number of scientific and commercial applications often require to analyse large data sets maintained over geographically distributed sites by using the computational power of distributed high-performance environments. Advances in networking technology and computational infrastructure made it possible to construct large-scale distributed computing platforms, called computational grids, that provide dependable, consistent, and pervasive access to high-end computational resources. Grids can play a significant role in providing an effective computational support for distributed data mining applications. Currently we are developing a software system for geographically distributed knowledge discovery applications called KNOWLEDGE GRID,which is designed on top of computational grid mechanisms, provided by grid environments such as Glob us. In this paper we present an integrated toolset named VEGA (Visual Environment for Grid Applications), which allows a Knowledge Grid user to develop and execute distributed data mining computations in a simple and effective way.

Distributed data mining on the grid

In many industrial, scientific and commercial applications, it is often necessary to analyze large data sets, maintained over geographically distributed sites, by using the computational power of distributed and parallel systems. The grid can play a significant role in providing an effective computational support for knowledge discovery applications. We describe a software architecture for geographically distributed high-performance knowledge discovery applications called K nowledge G rid , which is designed on top of computational grid mechanisms, provided by grid environments such as Globus. The K nowledge G rid uses the basic grid services such as communication, authentication, information, and resource management to build more specific parallel and distributed knowledge discovery tools and services. The paper discusses how the K nowledge G rid can be used to implement distributed data mining services.

Shared State for Distributed Interactive Data Mining Applications

Distributed data mining applications involving user interaction are now feasible due to advances in processor speed and network bandwidth. These applications are traditionally implemented using ad-...