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Portions of this document may be iIIegiilt, in dectronic image prodods Images are. pmduced fram the best available original, Scalable Distributed Data Mining Using An Agent. Based Architecture, Hillol Kargupta Ilker Hamzaoglu Brian Stafford. Computational Science Methods Group, X Division Los Alamos National Laboratory. P O Box 1663 MS F645, Los Alamos NM 87545, L A UR 96 3491.
Abstract agent based parallel data mining system In. PADMA individual agents are responsible for lo, Algorithm scalability and the distributed nature cal data accessing collaborative data analysis. of both data and computation deserve serious at and web based interactive information visual. tention in the context of data mining This paper ization Although PADMA architecture is not. presents PADMA PArallel Data Mining Agents specific to any particular domain and currently. a parallel agent based system that makes an ef PADMA is being enhanced to handle both text. fort to address these issues PADMA contains and numeric data in this paper we describe only. modules for 1 parallel data accessing opera the initial implementation of PADMA for un. tions 2 parallel hierarchical clustering and 3 structured text data mining. web based data visualization This paper de Section 2 introduces previous work on agent. scribes the general architecture of PADMA and based software systems and parallel data min. experimental results ing Section 3 presents a general overview of. the PADMA system The parallel relational, 1 Introduction database accessing operations of PADMA agents. are described in Section 4 Section 5 describes, Data mining involves extraction transformation the representation scheme of text documents and. and presentation of data in useful form As we the hierarchical clustering algorithm incorporated. move more and more toward a paper less society in the agents Section 6 describes the web. each of these components of data mining is likely based user interface and visualization module of. to face the challenges of dealing with large volume PADMA Section 7 presents experimental results. of data Apart from the sheer volume of the data on grounds of scalability and summarizes some. the very distributed nature of the data storage applications of PADMA in health informatics. and computing environments is likely to play an Section 8 concludes this paper and identifies the. important role in the design of next generation of on going work. data mining systems, In this paper we explore the possibility of large. scale data mining using a very distributed in 2 Related Work. formation processing architecture We present, PADMA PArallel Data Mining Agents an Although the motivation behind the initial de.
velopment of PADMA came from many different, The author c a n be reached by email to hillol lanl gov domains the main methodological approach was. based on two growing fields of computing 1 Disk, agent based information processing architecture e e. and 2 parallel computing In this section we, briefly review previous efforts made in the area. of data mining using the above mentioned tech, Interest in agent based software systems soared. high during the last few years An introduction to, intelligent agents can be found elsewhere Maes.
1994 Foner 1993 The demand for adaptive and, smarter software system lead to the incorporation. of intelligent agent based technology for address, ing many different problems such as automated Application. mail filtering Maes 1994 Lashkari Metral User Request Result. Maes 1994 meeting scheduling Kozierok, Maes 1993 Software agents are also used. for aiding information retrieval and processing User Interface. to extract higher level information Moukas, 1996 reported the Amalthaea system that uses. agents to discover and filter information avail, able in the world wide web Amalthaea also used Figure 1 The PADMA architecture.
evolutionary learning algorithms for generating, new agents The efficacy of agents was evalu. ated by the feedback from the user McElligott mining it effectively demonstrated the use of par. and Sorensen 1994 proposed an evolutionary allel computing technology for processing large. connectionist approach for information filtering amount of information Shek Mesrobian and. Their approach suggested using machine learning Muntz 1996 reported the Conquest system for. algorithms to learn suitable representation of the parallel data mining of distributed geoscientific. text documents and used feedback from the user data This system exploits parallel query pro. for supervised learning cessing distributed data accessing capabilities for. geoscientific data mining A genetic algorithm, Parallel data mining is a growing field that based parallel data mining system called GA. tries to exploit the benefits of parallel comput MINER is reported elsewhere Radcliffe 1995. ing for mining large scale databases Holsheimer This system first determines a suitable represen. Kersten and Siebes 1996 developed a parallel tation of data and then uses a parallel genetic al. data mining tool Data Surveyor that consists gorithm to detect patterns in the data The scal. of a mining tool and a parallel database server ability of the system was investigated for shared. It supported regular parallel database operations and distributed memory machines. and mechanisms for higher level rule induction PADMA combines many features of the agent. Parallel algorithms for inducing association rules based and parallel data mining systems The fol. have been reported elsewhere Zaki Ogihara lowing section presents an overview of PADMA. Parthasarathy Li 1996 They primarily fo, cused on optimization issues for parallel rule in. duction algorithms The PARKA project An 3 Architecture Of PADMA. derson Hendler Evett Kettler 1994 is an, other example of exploiting the strengths of par The PADMA is an agent based architecture for. allel computing for processing knowledge bases parallel distributed data mining The goal of this. Although the knowledge base of PARKA is not effort is to develop a flexible system that will ex. exactly same as the usual databases used in data ploit data mining agents in parallel for the par. ticular application in hand Although PADMA distributed memory machine provided that MPI. is h o t specialized for any particular kind of data is operational on this machine and a unix file sys. mining domain its initial implementation used tem is used for serial input output operations on. agents specializing in unstructured text docu its nodes The user interface is written for Java. ment classification Figure 1shows the overall ar sensitive browser PADMA can be functionally. chitecture of PADMA The main structural com decomposed into three different components 1. ponents of PADMA are 1 data mining agents parallel query processing and data accessing 2. 2 facilitator for coordinating the agents and 3 hierarchical clustering and 3 interactive clus. user interface Each of these items are described ter data visualization Each of these components. in the following of PADMA will be further elaborated in the fol. Data mining agents are responsible for access lowing sections. ing data and extracting higher level useful in, formation from the data A data mining agent.
specializes in performing some activity in the do 4 Parallel Data Accessing Oper. main of interest In the current implementation ations By Agents. data mining agents specializes on text analysis, and classification Accessing data is an important aspect of data. Agents work in parallel and share their infor mining In large scale data mining data access. mation through the facilitator The facilitator input output performance becomes a critical fac. module coordinates the agents presents informa tor in the overall performance of the data mining. tion to the user interface and provides feedbacks system Accessing data in parallel may help de. to the agents from the user creasing the response time Dewitt Gray 1992. PADMA has a graphical web based user inter In PADMA each data mining agent maintains. face for presenting information extracted by the its own disk subsystem to carry out input output. agents to the user The facilitator accepts queries operations locally This provides parallel data ac. from the user interface in standard SQL Struc cess for the whole system Currently striped and. tured Query Language format the queries are blocked data distribution algorithms are used to. broadcasted to the agents Agents comes up with distributed documents across data mining agents. the extracted information relevant to the query Each agent and the facilitator also maintain a file. Facilitator collects the information and presents cache for caching the documents that they ac. it to the user cess Appropriate buffer management algorithms. The agents and facilitator of PADMA are de e g FIFO replacement policy write back and. veloped using a Parallel Portable File System prefetching are employed to maximize the bene. PPFS Parallel Portable File System PPFS fit obtained from these caches. user level library was developed in the Com Data mining agents in PADMA also provide. puter Science department in University of Illi parallel relational database functionality This. nois at Urbana Champaign Huber Elford Reed is achieved by storing each corpus which con. Chien Blumenthal 1995 Huber 1995 The sists of a number of text documents as a re. PADMA is designed in object oriented style to lational database table with document number. provide an extensible infrastructure and coded in tezt ngmm vector attributes Currently a sub. C MPI Message Passing Interface is used set of SQL Structured Query Language is sup. as the message passing substrate for interprocess ported by PADMA These include table creation. communication Each data mining agent uses the and deletion hash index creation and deletion. underlying unix file system on the machines they parallel select and join operations PADMA. are executing on for carrying out their local in achieves parallel query processing through intra. put output operations PADMA currently runs operator parallelism. on a cluster of Sun Sparc workstations and on This functionality is provided to help the users. IBM SP 2 However it is easily portable to any to select the subset of the documents they want. to explore with clustering PADMA provides a O n Hash join algorithm performs better than. special condition in an SQL query which helps the sort merge join for equijoin operations unless. the users to select the documents related to a the tables are already in sorted order However it. keyword For example NGRAM ELECTRON is ineffective for non equijoin operations In or. condition can be used to select the data sets with der to effectively support both equijoin and non. the NGRAM feature instantiated to the keyword equijoin operations sort merge join algorithm is. ELECTRON Using this special condition users implemented in PADMA. can analyze data related to a keyword in two dif A fragment and replicate broadcast strategy. ferent ways In the first method PADMA can be is utilized to parallelize the sort merge join algo. used to create a new table based on the outcome rithm Each data mining agent initially sorts its. of a query and then this new table can be an part of both tables and compares these parts. alyzed by PADMA agents The second method Each agent then broadcasts its part of the small. achieves the same functionality on the fly i e size table to the other agents After each agent. without creating a new table This is done by compares its part of the larger table against the. the query and cluster we refer to the analy tuples of the smalltable it received from the other. sis part by cluster operation since the initial im agents the results are gathered by the facilitator. plementation of PADMA had only unsupervised which produces the final outcome of the join o p. cluster analysis capability operation which com eration by merging these individual results. bines the querying and clustering operations by, first executing the query operation on the agents. and then feeding the selected data directly to the 5 Parallel Data Analysis By. analysis module This is a much more scalable al Agents. gorithm compared to the first one since it doesn t. involve communicating with the facilitator except In PADMA data analysis is primarily done by the. reporting the final results We used this method agents in a distributed fashion Every agent re. in the performance experiments turns a concept graph to the facilitator which. could be null if an agent does not find anything, Parallel select operations in PADMA are car. relevant to the user s query The facilitator is re. ried out independently by each data mining agent, sponsible for combining the concept graphs and. without any interprocess communication After, present the result to the interface in a user trans.
each agent is carried out the select operation on, parent manner. its local data the results are gathered by the fa, Although PADMA agents are currently being. cilitator which produces the final outcome of the, provided with numeric data analysis algorithms. select operation by merging these individual re, experimental results reported here were produced. using agents that are capable of analyzing un, There are three major algorithms for imple structured textual data PADMA agents uses.
menting join operations between two tables De both su. important role in the design of next generation of collaborative data analysis evolutionary learning algorithms for generating

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