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Abstract Announcement for International Journal of Data Warehousing and Mining (IJDWM) 11(1)
The contents of the latest issue of:
International Journal of Data Warehousing and Mining (IJDWM)
Impact Factor: 0.786
Volume 11, Issue 1, January - March 2015
Published: Quarterly in Print and Electronically
ISSN: 1548-3924; EISSN: 1548-3932; 
Published by IGI Global Publishing, Hershey, USA
www.igi-global.com/ijdwm
Editor(s)-in-Chief: David Taniar (Monash University, Australia)

Note: There are no submission or acceptance fees for manuscripts submitted to the International Journal of Data Warehousing and Mining (IJDWM). All manuscripts are accepted based on a double-blind peer review editorial process.
ARTICLE 1

Updating the Built Prelarge Fast Updated Sequential Pattern Trees with Sequence Modification

Jerry Chun-Wei Lin (Innovative Information Industry Research Center (IIIRC) & School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen, China), Wensheng Gan (Innovative Information Industry Research Center (IIIRC) & School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen, China), Tzung-Pei Hong (Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan & Department of Computer Science and Engineering, National SunYat-sen University, Kaohsiung, Taiwan), Jingliang Zhang (Shandong Sport University, Jinan, China)

Mining useful information or knowledge from a very large database to aid managers or decision makers to make appropriate decisions is a critical issue in recent years. Sequential patterns can be used to discover the purchased behaviors of customers or the usage behaviors of users from Web log data. Most approaches process a static database to discover sequential patterns in a batch way. In real-world applications, transactions or sequences in databases are frequently changed. In the past, a fast updated sequential pattern (FUSP)-tree was proposed to handle dynamic databases whether for sequence insertion, deletion or modification based on FUP concepts. Original database is required to be re-scanned if it is necessary to maintain the small sequences which was not kept in the FUSP tree. In this paper, the prelarge concept was adopted to maintain and update the built prelarge FUSP tree for sequence modification. A prelarge FUSP tree is modified from FUSP tree for preserving not only the frequent 1-sequences but also the prelarge 1-sequences in the tree structure. The PRELARGE-FUSP-TREE-MOD maintenance algorithm is proposed to reduce the rescans of the original database due to the pruning properties of prelarge concept. When the number of modified sequences is smaller than the safety bound of the prelarge concept, better results can be obtained by the proposed PRELARGE-FUSP-TREE-MOD maintenance algorithm for sequence modification in dynamic databases.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/updating-the-built-prelarge-fast-updated-sequential-pattern-trees-with-sequence-modification/122513

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=122513

ARTICLE 2

Parallel Real-Time OLAP on Multi-Core Processors

Frank Dehne (School of Computer Science, Carleton University, Ottawa, Canada), Hamidreza Zaboli (School of Computer Science, Carleton University, Ottawa, Canada)

One of the most powerful and prominent technologies for knowledge discovery in decision support systems is online analytical processing (OLAP). Most of the traditional OLAP research, and most of the commercial systems, follow the static data cube approach proposed by Gray et.al. and materialize all or a subset of the cuboids of the data cube in order to ensure adequate query performance. Practitioners have called for some time for a real-time OLAP approach where the OLAP system gets updated instantaneously as new data arrives and always provides an up-to-date data warehouse for the decision support process. However, a major problem for real-time OLAP is the significant performance issues with large scale data warehouses. The aim of our research is to address these problems through the use of efficient parallel computing methods. In this paper, we present a parallel real-time OLAP system for multi-core processors. To our knowledge, this is the first real-time OLAP system that has been parallelized and optimized for contemporary multi-core architectures. Our system allows for multiple insert and multiple query transactions to be executed in parallel and in real-time. We evaluated our method for a multitude of scenarios (different ratios of insert and query transactions, query transactions with different amounts of data aggregation, different database sizes, etc.), using the TPCDS “Decision Support” benchmark data set. As multi-core test platforms, we used an Intel Sandy Bridge processor with 4 cores (8 hardware supported threads) and an Intel Xeon Westmere processor with 20 cores (40 hardware supported threads). The tests demonstrate that, with increasing number of processor cores, our parallel system achieves close to linear speedup in transaction response time and transaction throughput. On the 20 core architecture we achieved, for a 100 GB database, a better than 0.25 second query response time for real-time OLAP queries that aggregate 25% of the database. Since hardware performance improvements are currently, and in the foreseeable future, achieved not by faster processors but by increasing the number of processor cores, our new parallel real-time OLAP method has the potential to enable OLAP systems that operate in real-time on large databases.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/parallel-real-time-olap-on-multi-core-processors/122514

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=122514

ARTICLE 3

TripRec: An Efficient Approach for Trip Planning with Time Constraints

Heli Sun (School of Electronic and Information Engineering, Xi'an Jiaotong Univeristy, Xi'an, China & State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing China), Jianbin Huang (State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China & School of Software, Xidian University, Xi'an, China), Xinwei She (School of Software, Xidian University, Xi'an, China), Zhou Yang (School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China), Jiao Liu (School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China), Jianhua Zou (School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China), Qinbao Song (School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China), Dong Wang (School of Information Science and Technology, Northwest University, Xi'an, China)

The problem of trip planning with time constraints aims to find the optimal routes satisfying the maximum time requirement and possessing the highest attraction score. In this paper, a more efficient algorithm TripRec is proposed to solve this problem. Based on the principle of the Aprior algorithm for mining frequent item sets, our method constructs candidate attraction sets containing k attractions by using the join rule on valid sets consisting of k-1 attractions. After all the valid routes from the valid k-1 attraction sets have been obtained, all of the candidate routes for the candidate k-sets can be acquired through a route extension approach. This method exhibits manifest improvement of the efficiency in the valid routes generation process. Then, by determining whether there exists at least one valid route, the paper prunes some candidate attraction sets to gain all the valid sets. The process will continue until no more valid attraction sets can be obtained. In addition, several optimization strategies are employed to greatly enhance the performance of the algorithm. Experimental results on both real-world and synthetic data sets show that our algorithm has the better pruning rate and efficiency compared with the state-of-the-art method.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/triprec/122515

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=122515

ARTICLE 4

iTrade: A Mobile Data-Driven Stock Trading System with Concept Drift Adaptation

Yong Hu (Institute of Business Intelligence and Knowledge Discovery, Guangdong University of Foreign Studies, Guangzhou, China & School of Business, Sun Yat-sen University, Guanghzhou, China), Xiangzhou Zhang (School of Business, Sun Yat-sen University, Guangzhou, China), Bin Feng (School of Management, Guangdong University of Foreign Studies, Guangzhou, China), Kang Xie (School of Business, Sun Yat-sen University, Guangzhou, China), Mei Liu (Department of Internal Medicine, University of Kansas Medical Center, Kansas City, KS, USA)

Among all investors in the Chinese stock market, more than 95% are non-professional individual investors. These individual investors are in great need of mobile apps that can provide professional and handy trading analysis and decision support everywhere. However, financial data is challenging to analyze because of its large-scale, non-linear and noisy characteristics in a varying stock environment. This paper develops a Mobile Data-Driven Stock Trading System (iTrade), which is a mobile app system based on Client-Server architecture and various data mining techniques. The iTrade is characterized by 1) a data-driven intelligent learning model, which can provide further insight compared to empirical technical analysis, 2) a concept drift adaptation process, which facilitates the model adaptation to market structure changes, and 3) a rigorous benchmark analysis, including the Buy-and-Hold strategy and the strategies of three world-famous master investors (e.g., Warren E. Buffett). Technologies used in iTrade include the Least Absolute Shrinkage and Selection Operator (Lasso) algorithm, Support Vector Machine (SVM) and risk-adjusted portfolio optimization. An application case of iTrade is presented, which is based on a seven-year (2005-2011) back-testing. Evaluation results indicated that iTrade could gain much higher cumulative return compared to the benchmark (Shanghai Composite Index). To the best of our knowledge, this is the first study and mobile app system that emphasizes and investigates the concept drift phenomenon in stock market, as well as the performance comparison between data-driven intelligent model and strategies of master investors.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/itrade/122516

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=122516

For full copies of the above articles, check for this issue of the International Journal of Data Warehousing and Mining (IJDWM) in your institution's library. This journal is also included in the IGI Global aggregated "InfoSci-Journals" database: www.igi-global.com/isj.
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Mission of IJDWM:

The International Journal of Data Warehousing and Mining (IJDWM) aims to publish and disseminate knowledge on an international basis in the areas of data warehousing and data mining. It is published multiple times a year, with the purpose of providing a forum for state-of-the-art developments and research, as well as current innovative activities in data warehousing and mining. In contrast to other journals, this journal focuses on the integration between the fields of data warehousing and data mining, with emphasize on the applicability to real world problems. The journal is targeted at both academic researchers and practicing IT professionals.

Indices of IJDWM:

ACM Digital Library
Australian Business Deans Council (ABDC)
Bacon's Media Directory
Burrelle's Media Directory
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Current Contents®/Engineering, Computing, & Technology
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Science Citation Index Expanded (SciSearch®)
SCOPUS
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Thomson Reuters
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Coverage of IJDWM:

The journal is devoted to the publications of high quality papers on theoretical developments and practical applications in data warehousing and data mining. Original research papers, state-of-the-art reviews, and technical notes are invited for publications.

The journal accepts paper submission of any work relevant to data warehousing and data mining. Special attention will be given to papers focusing on mining of data from data warehouses; integration of databases, data warehousing, and data mining; and holistic approaches to mining and archiving data.

A summary of the scope of data warehousing and mining includes:

Data Warehousing:

Data mart
Data models
Data structures
Data warehousing process
Design
Online analytical process
Practical issues
Tools and languages

Data Mining:
Algorithms
Applications issues
Data mining methods
Knowledge discovery process
Mining databases
Tools and languages
Interested authors should consult the journal's manuscript submission guidelines www.igi-global.com/calls-for-papers/international-journal-data-warehousing-mining/1085