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Smart camp building scalable and highly available IT-infrastructures

Sergej Proskurin, David McMeekin, Achim P. Karduck
2012 2012 6th IEEE International Conference on Digital Ecosystems and Technologies (DEST)  
One possible solution is to design a system in the context of high availability and horizontal scalability.  ...  The Western Australian resources boom has created a demand for a large amount of domestic accommodations, known as mining camps.  ...  Many thanks to Professor Elizabeth Chang for providing with DEBII all the support of an inspiring environment.  ... 
doi:10.1109/dest.2012.6227923 dblp:conf/dest/ProskurinMK12 fatcat:3s4faekbojhzdllgcbokzs7yuq

Scalable mining of large disk-based graph databases

Chen Wang, Wei Wang, Jian Pei, Yongtai Zhu, Baile Shi
2004 Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '04  
As many graph databases in applications cannot be held into main memory, scalable mining of large, disk-based graph databases remains a challenging problem.  ...  The experimental results show that the new index structure enables the scalable graph pattern mining over large databases.  ...  Jiawei Han for kindly providing us the executable of gSpan and answering our questions promptly.  ... 
doi:10.1145/1014052.1014088 dblp:conf/kdd/WangWPZS04 fatcat:ve2pohskdjb33hos4ubqo7impq

Eclat with Large Data base Parallel Algorithm and Improve its Efficiency

Rana Ishita, Amit Rathod
2016 International Journal of Computer Applications  
Association rule mining finding frequent pattern, correlations among the items or object in transactional database or relational database.  ...  Data mining is the finding the hidden pattern from the huge amount of data.  ...  Eclat algorithm is the best known basic algorithm for mining frequent item sets in a set of transaction. Eclat represents the data in vertical data format.  ... 
doi:10.5120/ijca2016910462 fatcat:l6hb7bisyffwvjbcswpeezuc6q

Privacy Preserving Combinatorial Function for Multi-Partitioned Data Sets

V. S.Prakash, A. Shanmugam, P. Murugesan
2012 International Journal of Computer Applications  
An improved amount and range of information stored in databases has direct to an enhancement in the desire for ranked and "best match" queries.  ...  To facilitate privacy preservation in data mining or machine learning algorithms over horizontally partitioned or vertically partitioned data, many protocols have been proposed using SMC and various secure  ...  That is, no other third party should involve in this sharing data between the users. Then User C and User D partition the data from the database in a vertical manner.  ... 
doi:10.5120/6281-8450 fatcat:u6hlf2f2kzbqrlp2tz53d3qavu

GraphMiner

Wei Wang, Chen Wang, Yongtai Zhu, Baile Shi, Jian Pei, Xifeng Yan, Jiawei Han
2005 Proceedings of the 2005 ACM SIGMOD international conference on Management of data - SIGMOD '05  
Recently, we developed an effective index structure, ADI, and efficient algorithms for mining frequent patterns from large, disk-based graph databases [5], as well as constraint-based mining techniques  ...  the comparison with some state-of-the-art methods, the constraint-based graph-pattern mining techniques and the procedure of constrained graph mining, as well as mining real data sets in novel applications  ...  The three-layer design makes the ADI structure highly flexible and scalable for graph-pattern mining.  ... 
doi:10.1145/1066157.1066273 dblp:conf/sigmod/WangWZSPYH05 fatcat:ycmphmqzujfaxg6qgkx3a4fuym

Scalable Frequent Itemset Mining Using Heterogeneous Computing : Parapriori Algorithm

V.B Nikam, B B Meshram
2014 International Journal of Distributed and Parallel systems  
Since high performance computing has many processors, and many cores, consistent runtime performance for such very large databases on association rules mining is achieved.  ...  Association Rule mining is one of the dominant tasks of data mining, which concerns in finding frequent itemsets in large volumes of data in order to produce summarized models of mined rules.  ...  Pradeep Gupta, and others, for providing support to complete our work.  ... 
doi:10.5121/ijdps.2014.5502 fatcat:iozy5bptizftddm3dl7d53miwi

Regular frequent crime pattern mining on crime datasets

G Vijay Kumar, M Sreedevi, G Vamsi Krishna, N Sai Ram
2018 International Journal of Engineering & Technology  
In this paper we are presenting anoth-er violation design called general incessant violation design which happens frequently at certain time interims utilizing vertical information arrange additionally  ...  The objective of violation information mining is to comprehend different violation designs in criminal conduct in request to foresee viola-tions and expect criminal movement to stay away from the violation  ...  Our algorithm performance is more efficient, and we included the vertical data format, so it is scalable and efficient over big databases.  ... 
doi:10.14419/ijet.v7i2.7.11438 fatcat:qxqfd3rxnrcixnyihz7fvapmwa

Review on Apriori Based Frequent Item Set Mining Using Various Techniques

Ravishankar Sahu
2018 International Journal for Research in Applied Science and Engineering Technology  
In any case, these instruments accompany their own technical difficulties, for example, balanced data distribution as well as inter-communication costs.  ...  Frequent Item set Mining is a standout amongst the most prominent systems to extract knowledge from data.  ...  Figure 1 delineates the design for distributed data mining. In the next section, we discuss about Hadoop technology, its architecture and its working in a distributed environment. II.  ... 
doi:10.22214/ijraset.2018.3109 fatcat:co65yda2nrgpbazv4au4iaceqm

Mining Uncertain Sequential Patterns in Iterative MapReduce [chapter]

Jiaqi Ge, Yuni Xia, Jian Wang
2015 Lecture Notes in Computer Science  
This paper proposes a sequential pattern mining (SPM) algorithm in large scale uncertain databases.  ...  scalability.  ...  Introduction Sequential pattern mining (SPM) is an important data mining application. It provides inter-transactional analysis for timestamped data which are modeled by sequence databases.  ... 
doi:10.1007/978-3-319-18032-8_19 fatcat:cu24t34rhnbqpb52wffxnwr4k4

Approaches to Parallel Graph-Based Knowledge Discovery

Diane J. Cook, Lawrence B. Holder, Gehad Galal, Ron Maglothin
2001 Journal of Parallel and Distributed Computing  
In particular, scientific discovery systems focus on the utilization of richer data representation, sometimes without regard for scalability.  ...  This research investigates approaches for scaling a particular knowledge discovery data mining system, Subdue, using parallel and distributed resources.  ...  INTRODUCTION One of the barriers to the integration of scientific discovery methods into practical data mining approaches is their lack of scalability.  ... 
doi:10.1006/jpdc.2000.1696 fatcat:vme4ebokxzakrksexggd7l7aem

An integrated, generic approach to pattern mining: data mining template library

Vineet Chaoji, Mohammad Al Hasan, Saeed Salem, Mohammed J. Zaki
2008 Data mining and knowledge discovery  
However, no practical framework for integrating the FPM tasks has been attempted. In this paper, we describe the design and implementation of the Data Mining Template Library (DMTL) for FPM.  ...  For example, the kind of mining approach to use, the kind of data types and formats to mine over, the kind of back-end storage manager to use, are all specified as a list of properties.  ...  Acknowledgment We would like to thank the anonymous reviewers for their inputs which have greatly helped us improve the quality of the paper.  ... 
doi:10.1007/s10618-008-0098-x fatcat:7ffj62b7zjgnzgrl3jzjb5655a

Data challenges at Yahoo!

Ricardo Baeza-Yates, Raghu Ramakrishnan
2008 Proceedings of the 11th international conference on Extending database technology Advances in database technology - EDBT '08  
These challenges have led to the development of new data systems and novel data mining techniques.  ...  In this short paper we describe the data that Yahoo! handles, the current trends in Web applications, and the many challenges that this poses for Yahoo! Research.  ...  Structured Data. Many Yahoo! verticals (e.g., Autos, Local, Personals, Shopping) rely upon structured listings databases. Streams. Many Yahoo!  ... 
doi:10.1145/1352431.1352509 fatcat:mylu5uo2dzed3aqrzemvbx2cci

Distributed Sequential Pattern Mining: A Survey and Future Scope

Suhasini Itkar, Uday Kulkarni
2014 International Journal of Computer Applications  
Distributed sequential pattern mining is the data mining method to discover sequential patterns from large sequential database on distributed environment.  ...  This paper presents a systematic review on work done for sequential pattern mining and advanced sequential pattern mining on distributed environment.  ...  Two types of grids are designed data grid which is used to retain and provide data for mining while other is computing grid which is used to perform computing related job in sequential pattern mining.  ... 
doi:10.5120/16461-6187 fatcat:7ggeuyoqwnfzhnhwjixufttpua

Various Research Opportunities in High Utility Itemset Mining

2019 International journal of recent technology and engineering  
Pattern mining is a technique, which discovers interesting, hidden, unpredicted and useful patterns of data from the database.  ...  Most of the research work in pattern mining has been focused on the traditional way of Frequent Itemset Mining (FIM) and Association Rule Mining (ARM) for patterndiscovery.  ...   Scalability Scalability is one of the core issues to be deal with to meet the new data challenges. Most of the HUIM algorithms have been developed for small databases.  ... 
doi:10.35940/ijrte.d7213.118419 fatcat:vrp53qrqq5fnhb27dh3xmi5a34

Data challenges at Yahoo!

Ricardo Baeza-Yates, Raghu Ramakrishnan
2008 Proceedings of the 11th international conference on Extending database technology Advances in database technology - EDBT '08  
These challenges have led to the development of new data systems and novel data mining techniques.  ...  In this short paper we describe the data that Yahoo! handles, the current trends in Web applications, and the many challenges that this poses for Yahoo! Research.  ...  Structured Data. Many Yahoo! verticals (e.g., Autos, Local, Personals, Shopping) rely upon structured listings databases. Streams. Many Yahoo!  ... 
doi:10.1145/1353343.1353421 dblp:conf/edbt/Baeza-YatesR08 fatcat:g56gk535xrgphmlyn6ocirno6u
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