scholarly journals Crime Busting Model Based on Dynamic Ranking Algorithms

2013 ◽  
Vol 2013 ◽  
pp. 1-10
Author(s):  
Yang Cao ◽  
Xiaotian Xu ◽  
Zhijing Ye
Keyword(s):  
Social Network ◽  
Large Datasets ◽  
Test Results ◽  
Priority List ◽  
Dynamic Algorithm ◽  
Semantic Learning ◽  
Network Mining ◽  
Ranking Algorithms ◽  
Great Performance ◽  
Theoretical Results

This paper proposed a crime busting model with two dynamic ranking algorithms to detect the likelihood of a suspect and the possibility of a leader in a complex social network. Signally, in order to obtain the priority list of suspects, an advanced network mining approach with a dynamic cumulative nominating algorithm is adopted to rapidly reduce computational expensiveness than most other topology-based approaches. Our method can also greatly increase the accuracy of solution with the enhancement of semantic learning filtering at the same time. Moreover, another dynamic algorithm of node contraction is also presented to help identify the leader among conspirators. Test results are given to verify the theoretical results, which show the great performance for either small or large datasets.

scholarly journals A graphical heuristic for reduction and partitioning of large datasets for scalable supervised training

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Sumedh Yadav ◽  
Mathis Bode
Keyword(s):  
Prediction Accuracy ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Large Datasets ◽  
Test Results ◽  
Accuracy Test ◽  
Reasonable Proportion ◽  
Speed Up ◽  
Run Time ◽  
Information Graph

Abstract A scalable graphical method is presented for selecting and partitioning datasets for the training phase of a classification task. For the heuristic, a clustering algorithm is required to get its computation cost in a reasonable proportion to the task itself. This step is succeeded by construction of an information graph of the underlying classification patterns using approximate nearest neighbor methods. The presented method consists of two approaches, one for reducing a given training set, and another for partitioning the selected/reduced set. The heuristic targets large datasets, since the primary goal is a significant reduction in training computation run-time without compromising prediction accuracy. Test results show that both approaches significantly speed-up the training task when compared against that of state-of-the-art shrinking heuristics available in LIBSVM. Furthermore, the approaches closely follow or even outperform in prediction accuracy. A network design is also presented for a partitioning based distributed training formulation. Added speed-up in training run-time is observed when compared to that of serial implementation of the approaches.

Dynamic Analysis and Experimental Research of Laser Cutting Machines

2014 ◽  
Vol 915-916 ◽  
pp. 31-34
Author(s):  
Qing Ping Zhang ◽  
Zheng Ru Wang ◽  
Yan Fang Wang
Keyword(s):  
Machine Tool ◽  
Modal Analysis ◽  
Laser Cutting ◽  
Machining Accuracy ◽  
Dynamics Analysis ◽  
Theoretical Base ◽  
Test Results ◽  
Cutting Machine ◽  
Manufacturing Errors ◽  
Theoretical Results

Vibration is one of the most important problems in laser cutting machine tool, which causes the manufacturing errors, also influences the machining accuracy of the parts. Modal analysis can calculate vibration type of structures. The paper presents how to use the powerful FEA software ANSYS to do the modal analysis on laser cutting machine tool and also studies the undamped free vibration on laser cutting machine tool. Finally, the test results and theoretical results were compared to verify the rationality of the modal, these provide theoretical base and conditions for dynamics analysis and optimal design.

An integrated social network mining for product-based technology analysis of Apple

2017 ◽  
Vol 117 (10) ◽  
pp. 2417-2430 ◽  
Author(s):  
Juhwan Kim ◽  
Sunghae Jun ◽  
Dong-Sik Jang ◽  
Sangsung Park
Keyword(s):  
Social Network ◽  
Product Development ◽  
New Product ◽  
Development Planning ◽  
Patent Analysis ◽  
Content Type ◽  
Network Mining ◽  
Technology Analysis ◽  
Social Network Mining ◽  
A Company

Purpose Patent contains vast information on developed technologies because of the patent system. So, it is important to analyze patent data for understanding technologies. Most previous studies on patent analysis were focused on the technology itself. Their research results lacked the consideration of products. But the patent analysis based on products is crucial for company because a company grows by sales of competitive products. The purpose of this paper is to propose a novel methodology of patent analysis for product-based technology. This study contributes to the product development strategy of a company. Design/methodology/approach The primary goal for developing technology is to release a new product. So it is important to analyze the technology based on the product. In this study, the authors analyze Apple’s technologies based in iPod, iPhone, and iPad. In addition, the authors propose a new methodology to analyze product-based technology. The authors call this an integrated social network mining (ISNM). In the ISNM, the authors carry out a social network analysis (SNA) according to each product of Apple, and integrate all SNA results of iPod, iPhone, and iPad using the technological keywords. Findings In this case study, the authors analyze Apple’s technologies according to Apple’s innovative products, such as the iPod, iPhone, and iPad. From the ISNM results of Apple’s technology, the authors can find which technological detail is more important in overall structure of Apple’s technologies. Practical implications This study contributes to the management of technology including new product development, technological innovation, and research and development planning. To know the technological relationship between whole technologies based on products can be the source of intensification of technological competitiveness. Originality/value Most of studies on technology analysis were focused on patent technology itself. Though one of their research goals was to develop new product, they had their limits considering the products because they did not use the technology information in the technology analysis. The originality of this research is to use the product information in technology analysis using the proposed ISNM.

Mining Organizations’ Networks

2011 ◽  
pp. 205-230 ◽  
Author(s):  
James A. Danowski
Keyword(s):  
Social Network ◽  
Oil Spill ◽  
Organizational Hierarchy ◽  
Art And Design ◽  
Bp Oil Spill ◽  
Network Mining ◽  
Social Network Mining ◽  
Corporate Innovation ◽  
Semantic Associations ◽  
Over Time

This chapter presents six examples of organization-related social network mining: 1) interorganizational and sentiment networks in the Deepwater BP Oil Spill events, 2) intraorganizational interdepartmental networks in the Savannah College of Art and Design (SCAD), 3) who-to-whom email networks across the organizational hierarchy the Ford Motor Company’s automotive engineering innovation: “Sync® w/ MyFord Touch”, 4) networks of selected individuals who left that organization, 5) semantic associations across email for a corporate innovation in that organization, and 6) assessment of sentiment across its email for innovations over time. These examples are discussed in terms of motivations, methods, implications, and applications.

Dynamics and Evolutional Patterns of Social Networks

2011 ◽  
pp. 156-170
Author(s):  
Yingzi Jin ◽  
Yutaka Matsuo
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Real World ◽  
Dynamic Network ◽  
Network Evolution ◽  
Belief Formation ◽  
Network Mining ◽  
Longitudinal Network Analysis ◽  
Relational Network

Previous chapters focused on the models of static networks, which consider a relational network at a given point in time. However, real-world social networks are dynamic in nature; for example, friends of friends become friends. Social network research has, in recent years, paid increasing attention to dynamic and longitudinal network analysis in order to understand network evolution, belief formation, friendship formation, and so on. This chapter focuses mainly on the dynamics and evolutional patterns of social networks. The chapter introduces real-world applications and reviews major theories and models of dynamic network mining.

A Microblog Classification Algorithm for Real-Time Search

2013 ◽  
Vol 411-414 ◽  
pp. 411-414
Author(s):  
Jing Bo Yuan ◽  
Bai Rong Wang ◽  
Ji Hao Yang ◽  
Shun Li Ding
Keyword(s):  
Social Network ◽  
Information Sharing ◽  
Real Time ◽  
Search Time ◽  
Classification Algorithm ◽  
Test Results ◽  
Question Classification ◽  
Query Classification

As a social network, microblog has obtained great attention and gotten wide application. Applications of microblog need to retrieve quickly information with the support of real-time search technology in order to implement information sharing. A query classification algorithm of microblog for real-time search was put forward. Based on question classification mechanism, the algorithm divides queries into two categories: the candidate queries and the popular queries, and takes separate storage strategy. Test results show that the classification algorithm can reduce real-time search time and improve the efficiency of retrieval.

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