Multidimensional Data Mining on the Early Scientific Talents of China

2020 ◽  
Author(s):  
Xingchen Liu ◽  
Boyu Zhao ◽  
Haotian Qian ◽  
Yuhang Liu
Keyword(s):  
Data Mining ◽  
Multidimensional Data

Mining Association Rules in Data Warehouses

2008 ◽  
pp. 303-335
Author(s):  
Haorianto Cokrowijoyo Tjioe ◽  
David Taniar
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Strategic Decision ◽  
Multidimensional Data ◽  
Data Warehouses ◽  
Multidimensional Databases ◽  
Efficient Data ◽  
Decision Making Processes ◽  
Mining Association Rules ◽  
Transactional Data

Data mining applications have enormously altered the strategic decision-making processes of organizations. The application of association rules algorithms is one of the well-known data mining techniques that have been developed to cope with multidimensional databases. However, most of these algorithms focus on multidimensional data models for transactional data. As data warehouses can be presented using a multidimensional model, in this paper we provide another perspective to mine association rules in data warehouses by focusing on a measurement of summarized data. We propose four algorithms — VAvg, HAvg, WMAvg, and ModusFilter — to provide efficient data initialization for mining association rules in data warehouses by concentrating on the measurement of aggregate data. Then we apply those algorithms both on a non-repeatable predicate, which is known as mining normal association rules, using GenNLI, and a repeatable predicate using ComDims and GenHLI, which is known as mining hybrid association rules.

Data Warehouse and Business Intelligence Systems in the Context of E-HRM

2009 ◽  
pp. 223-229
Author(s):  
Martin Burgard ◽  
Franca Piazza
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Resource Management ◽  
Human Resource Management ◽  
Data Warehouse ◽  
Human Resource ◽  
Business Intelligence ◽  
Multidimensional Data ◽  
Business Intelligence Systems ◽  
Analytical Processing

The increased use of information technology leads to the generation of huge amounts of data which have to be stored and analyzed by appropriate systems. Data warehouse systems allow the storage of these data in a special multidimensional data base. Based on a data warehouse, business intelligence systems provide different analysis methods such as online analytical processing (OLAP) and data mining to analyze these data. Although these systems are already widely used and the usage is still growing, their application in the area of electronic human resource management (e-HRM) is rather scarce. Therefore, the objective of this article is to depict the components and functionality of these systems and to illustrate the application possibilities and benefits of these systems by selected application examples in the context of e-HRM.

An Envisioned Approach for Modeling and Supporting User-Centric Query Activities on Data Warehouses

2013 ◽  
Vol 9 (2) ◽  
pp. 89-109 ◽  
Author(s):  
Marie-Aude Aufaure ◽  
Alfredo Cuzzocrea ◽  
Cécile Favre ◽  
Patrick Marcel ◽  
Rokia Missaoui
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Information Retrieval ◽  
Query Processing ◽  
Data Warehouse ◽  
Multidimensional Data ◽  
Data Warehouses ◽  
Cooperative Query Answering ◽  
User Centric ◽  
Use Of Knowledge

In this vision paper, the authors discuss models and techniques for integrating, processing and querying data, information and knowledge within data warehouses in a user-centric manner. The user-centric emphasis allows us to achieve a number of clear advantages with respect to classical data warehouse architectures, whose most relevant ones are the following: (i) a unified and meaningful representation of multidimensional data and knowledge patterns throughout the data warehouse layers (i.e., loading, storage, metadata, etc); (ii) advanced query mechanisms and guidance that are capable of extracting targeted information and knowledge by means of innovative information retrieval and data mining techniques. Following this main framework, the authors first outline the importance of knowledge representation and management in data warehouses, where knowledge is expressed by existing ontology or patterns discovered from data. Then, the authors propose a user-centric architecture for OLAP query processing, which is the typical applicative interface to data warehouse systems. Finally, the authors propose insights towards cooperative query answering that make use of knowledge management principles and exploit the peculiarities of data warehouses (e.g., multidimensionality, multi-resolution, and so forth).

Constraint-based, multidimensional data mining

Computer ◽  
1999 ◽  
Vol 32 (8) ◽  
pp. 46-50 ◽  
Author(s):  
Jiawei Han ◽  
L.V.S. Lakshmanan ◽  
R.T. Ng
Keyword(s):  
Data Mining ◽  
Multidimensional Data

scholarly journals Application of Data Mining Technology in the Subject Tactical Teaching of Badminton

2018 ◽  
Vol 13 (07) ◽  
pp. 30 ◽  
Author(s):  
Delin Yang
Keyword(s):  
Data Mining ◽  
Incomplete Data ◽  
Training Programs ◽  
Point Of View ◽  
Multidimensional Data ◽  
Simulation Experiments ◽  
Mining Technology ◽  
Teaching Plan ◽  
The Subject ◽  
Case Data

In badminton field tactics teaching, there are problems of not timely, inaccu-rate and incomplete data collection of completion cases, not deep enough da-ta and too few personalized training programs for athletes' tactics. Aiming at solving the above problems, the traditional Apriori association algorithm was optimized, and the algorithm was applied to the acquisition of badminton field tactics case data. Moreover, the multidimensional data mining was con-ducted, thus providing a scientific and personalized teaching plan for bad-minton field tactical teaching. The simulation experiments showed that data mining technology applied to badminton field tactics teaching could effec-tively put forward personalized teaching plan for data support from the tacti-cal point of view, and improve the effect of badminton field tactical teach-ing.

scholarly journals Data mining in web personalization using the blended deep learning model

2020 ◽  
Vol 20 (3) ◽  
pp. 1507
Author(s):  
Qusay Abdullah Abed ◽  
Osamah Mohammed Fadhil ◽  
Wathiq Laftah Al-Yaseen
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Web Application ◽  
Large Data ◽  
Learning Model ◽  
Multidimensional Data ◽  
Data Sets ◽  
Web Personalization ◽  
Deep Learning Model ◽  
The Web

In general, multidimensional data (mobile application for example) contain a large number of unnecessary information. Web app users find it difficult to get the information needed quickly and effectively due to the sheer volume of data (big data produced per second). In this paper, we tend to study the data mining in web personalization using blended deep learning model. So, one of the effective solutions to this problem is web personalization. As well as, explore how this model helps to analyze and estimate the huge amounts of operations. Providing personalized recommendations to improve reliability depends on the web application using useful information in the web application. The results of this research are important for the training and testing of large data sets for a map of deep mixed learning based on the model of back-spread neural network. The HADOOP framework was used to perform a number of experiments in a different environment with a learning rate between -1 and +1. Also, using the number of techniques to evaluate the number of parameters, true positive cases are represent and fall into positive cases in this example to evaluate the proposed model.

scholarly journals Hybrid Algorithm for Anomaly Removal in Time Series Data Mining

2021 ◽  
Author(s):  
Abdul Razaque ◽  
Marzhan Abenova ◽  
Munif Alotaibi ◽  
Bandar Alotaibi ◽  
Hamoud Alshammari ◽  
...  
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Hybrid Algorithm ◽  
Time Series Data ◽  
State Of The Art ◽  
Large Data ◽  
Series Data ◽  
Multidimensional Data ◽  
Search Problem ◽  
Short Text

Time series data are significant and are derived from temporal data, which involve real numbers representing values collected regularly over time. Time series have a great impact on many types of data. However, time series have anomalies. We introduce hybrid algorithm named novel matrix profile (NMP) to solve the all-pairs similarity search problem for time series data. The proposed NMP inherits the features from two state-of-the art algorithms: similarity time-series automatic multivariate prediction (STAMP), and short text online microblogging protocol (STOMP). The proposed algorithm caches the output in an easy-to-access fashion for single- and multidimensional data. The proposed NMP algorithm can be used on large data sets and generates approximate solutions of high quality in a reasonable time. The proposed NMP can also handle several data mining tasks. It is implemented on a Python platform. To determine its effectiveness, it is compared with the state-of-the-art matrix profile algorithms i.e., STAMP and STOMP. The results confirm that the proposed NMP provides higher accuracy than the compared algorithms.

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