Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . solidifies the understanding of the concepts being presented."IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.

Fundamentals of Queueing Theory

Matrix Factorization Techniques for Recommender Systems

我的台灣, 看見心靈的故鄉 =2009林磐聳藝術與設計展

In this paper, we introduce an anonymous batch authenticated and key agreement (ABAKA) scheme to authenticate multiple requests sent from different vehicles and establish different session keys for different vehicles at the same time. In vehicular ad hoc networks (VANETs), the speed of a vehicle is changed from 10 to 40 m/s (36-144 km/h); therefore, the need for efficient authentication is inevitable. Compared with the current key agreement scheme, ABAKA can efficiently authenticate multiple requests by one verification operation and negotiate a session key with each vehicle by one broadcast message. Elliptic curve cryptography is adopted to reduce the verification delay and transmission overhead. The security of ABAKA is based on the elliptic curve discrete logarithm problem, which is an unsolved NP-complete problem. To deal with the invalid request problem, which may cause the batch verification fail, a detection algorithm has been proposed. Moreover, we demonstrate the efficiency merits of ABAKA through performance evaluations in terms of verification delay, transmission overhead, and cost for rebatch verifications, respectively. Simulation results show that both the message delay and message loss rate of ABAKA are less than that of the existing elliptic curve digital signature algorithm (ECDSA)-based scheme.

https://ir.nctu.edu.tw:443/bitstream/11536/26108/1/000286385700023.pdf

ABAKA: An Anonymous Batch Authenticated and Key Agreement Scheme for Value-Added Services in Vehicular Ad Hoc Networks

High utility sequential pattern mining is an emerging topic in pattern mining, which refers to identify sequences with high utilities (e.g., profits) but probably with low frequencies. To identify high utility sequential patterns, due to lack of downward closure property in this problem, most existing algorithms first generate candidate sequences with high sequence-weighted utilities (SWUs), which is an upper bound of the utilities of a sequence and all its supersequences, and then calculate the actual utilities of these candidates. This causes a large number of candidates since SWU is usually much larger than the real utilities of a sequence and all its supersequences. In view of this, we propose two tight utility upper bounds, prefix extension utility and reduced sequence utility, as well as two companion pruning strategies, and devise HUS-Span algorithm to identify high utility sequential patterns by employing these two pruning strategies. In addition, since setting a proper utility threshold is usually difficult for users, we also propose algorithm TKHUS-Span to identify top-k high utility sequential patterns by using these two pruning strategies. Three searching strategies, guided depth-first search (GDFS), best-first search (BFS) and hybrid search of BFS and GDFS, are also proposed to improve the efficiency of TKHUS-Span. Experimental results on some real and synthetic datasets show that HUS-Span and TKHUS-Span with strategy BFS are able to generate less candidate sequences and thus outperform other prior algorithms in terms of mining efficiency.

On efficiently mining high utility sequential patterns

The eHealth trend has spread globally. Internet of Things (IoT) devices for medical service and pervasive Personal Health Information (PHI) systems play important roles in the eHealth environment. A cloud-based PHI system appears promising but raises privacy and information security concerns. We propose a cloud-based fine-grained health information access control framework for lightweight IoT devices with data dynamics auditing and attribute revocation functions. Only symmetric cryptography is required for IoT devices, such as wireless body sensors. A variant of ciphertext-policy attribute-based encryption, dual encryption, and Merkle hash trees are used to support fine-grained access control, efficient dynamic data auditing, batch auditing, and attribute revocation. Moreover, the proposed scheme also defines and handles the cloud reciprocity problem wherein cloud service providers can help each other avoid fines resulting from data loss. Security analysis and performance comparisons show that the proposed scheme is an excellent candidate for a cloud-based PHI system.

Cloud-Based Fine-Grained Health Information Access Control Framework for LightweightIoT Devices with Dynamic Auditing andAttribute Revocation

Data broadcast is a promising technique to improve the bandwidth utilization and conserve the power consumption in a mobile computing environment. In many applications, the data items broadcast are dependent upon one another. However, most prior studies on broadcasting dependent data are restricted to a single broadcast channel environment, and as a consequence, the results are of limited applicability to the upcoming mobile environments. In view of this, we relax this restriction and explore the problem of broadcasting dependent data in multiple broadcast channels. By analyzing the model of dependent data broadcasting, we derive several theoretical properties for the average access time in a multiple channel environment. In light of the theoretical results, we develop a genetic algorithm to generate broadcast programs. Our experimental results show that the theoretical results derived are able to guide the search of the genetic algorithm very effectively, thus leading to broadcast programs of very high quality.

/pdf/dependent-data-broadcasting-for-unordered-queries-in-a-24t7x9c81d.pdf

Dependent data broadcasting for unordered queries in a multiple channel mobile environment

We explore in this paper a practicably interesting mining task to retrieve top-k (closed) itemsets in the presence of the memory constraint. Specifically, as opposed to most previous works that concentrate on improving the mining efficiency or on reducing the memory size by best effort, we first attempt to specify the available upper memory size that can be utilized by mining frequent itemsets. To comply with the upper bound of the memory consumption, two efficient algorithms, called MTK and MTK_Close, are devised for mining frequent itemsets and closed itemsets, respectively, without specifying the subtle minimum support. Instead, users only need to give a more human-understandable parameter, namely the desired number of frequent (closed) itemsets k. In practice, it is quite challenging to constrain the memory consumption while also efficiently retrieving top-k itemsets. To effectively achieve this, MTK and MTK_Close are devised as level-wise search algorithms, where the number of candidates being generated-and-tested in each database scan will be limited. A novel search approach, called ?-stair search, is utilized in MTK and MTK_Close to effectively assign the available memory for testing candidate itemsets with various itemset-lengths, which leads to a small number of required database scans. As demonstrated in the empirical study on real data and synthetic data, instead of only providing the flexibility of striking a compromise between the execution efficiency and the memory consumption, MTK and MTK_Close can both achieve high efficiency and have a constrained memory bound, showing the prominent advantage to be practical algorithms of mining frequent patterns.

https://ir.nctu.edu.tw:443/bitstream/11536/8530/1/000257396700017.pdf

Jiun-Long Huang

Papers

ABAKA: An Anonymous Batch Authenticated and Key Agreement Scheme for Value-Added Services in Vehicular Ad Hoc Networks

On efficiently mining high utility sequential patterns

Cloud-Based Fine-Grained Health Information Access Control Framework for LightweightIoT Devices with Dynamic Auditing andAttribute Revocation

Dependent data broadcasting for unordered queries in a multiple channel mobile environment

Mining top-k frequent patterns in the presence of the memory constraint