Transactions on Large-Scale Data- and Knowledge-Centered Systems XVI

Selected Papers from ACOMP 2013

herausgegeben von: Abdelkader Hameurlain, Josef Küng, Roland Wagner, Tran Khanh Dang, Nam Thoai

Verlag: Springer Berlin Heidelberg

Buchreihe : Lecture Notes in Computer Science

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. Current decentralized systems still focus on data and knowledge as their main resource. Feasibility of these systems relies basically on P2P (peer-to-peer) techniques and the support of agent systems with scaling and decentralized control. Synergy between grids, P2P systems, and agent technologies is the key to data- and knowledge-centered systems in large-scale environments.

This, the 16th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains extended and revised versions of 7 papers, selected from the 30 papers presented at the International Conference on Advanced Computing and Applications, ACOMP 2013, held October 23-25, 2013, in Ho Chi Minh City, Vietnam. Topics covered include data engineering, information retrieval, query processing and optimization, energy-efficient resource allocation, and security and privacy.

Inhaltsverzeichnis

Frontmatter

Visualizing Web Attack Scenarios in Space and Time Coordinate Systems

Abstract

Intrusion Detection Systems can detect attacks and notify responsible people of these events automatically. However, seeing individual attacks, although useful, is often not enough to understand about the whole attacking process as well as the skills and motivations of the attackers. Attacking step is usually just a phase in the whole intrusion process, in which attackers gather information and prepare required conditions before executing it, and clear log records to hide their traces after executing it. Current approaches to constructing attack scenarios require pre-defining of cause and effect relationships between events, which is a difficult and time-consuming task. In this work, we exploit the linking nature between pages in web applications to propose an attack scenario construction technique without the need of cause and effect relationships pre-definition. Built scenarios are then visualized in space and time coordinate systems to support viewing and analysis. We also develop a prototype implementation based on the proposal and use it to experiment with different simulated attack scenarios.

Tran Tri Dang, Tran Khanh Dang

Question-Answering for Agricultural Open Data

Abstract

In the agricultural sector, the improvement of productivity and quality with respect to such attributes as safety, security and taste has been required in recent years. We aim to contribute to such improvement through the application of Information and Communication Technology (ICT). In this paper, we first propose a model of agricultural knowledge by Linked Open Data (LOD) with a view to establishing an open standard for agricultural data, allowing flexible schemas based on ontology alignment. We also present a semi-automatic mechanism that we developed to extract agricultural knowledge from the Web, which involves a bootstrapping method and dependency parsing, and confirmed a certain degree of accuracy. Moreover, we present a voice-controlled question-answering system that we developed for the LOD using triplification of query sentences and graph pattern matching of the triples. Finally, we confirm through a use case that users can obtain the necessary knowledge for several problems encountered in the agricultural workplace.

Takahiro Kawamura, Akihiko Ohsuga

Learning-Oriented Question Recommendation Using Bloom’s Learning Taxonomy and Variable Length Hidden Markov Models

Abstract

The information overload in the past two decades has enabled question-answering (QA) systems to accumulate large amounts of textual fragments that reflect human knowledge. Therefore, such systems have become not just a source for information retrieval, but also a means towards a unique learning experience. Recently developed recommendation techniques for search engine queries try to leverage the order in which users navigate through them. Although a similar approach might improve the learning experience with QA systems, questions would still be considered as abstract objects, without any content or meaning. In this paper, a new learning-oriented technique is defined that exploits not only the user’s history log, but also two important question attributes that reflect its content and purpose: the topic and the learning objective. In order to do this, a domain-specific topic-taxonomy and Bloom’s learning framework is employed, whereas for modeling the order in which questions are selected, variable length Markov chains (VLMC) are used. Results show that the learning-oriented recommender can provide more useful, meaningful recommendations for a better learning experience than other predictive models.

Hilda Kosorus, Josef Küng

On the Performance of Triangulation-Based Multiple Shooting Method for 2D Geometric Shortest Path Problems

Abstract

In this paper we describe an algorithm based on the idea of the direct multiple shooting method for solving approximately 2D geometric shortest path problems (introduced by An et al. in Journal of Computational and Applied Mathematics, 244 (2103), pp. 67-76). The algorithm divides the problem into suitable sub-problems, and then solves iteratively sub-problems. A so-called collinear condition for combining the sub-problems was constructed to obtain an approximate solution of the original problem. We discuss here the performance of the algorithm. In order to solve the sub-problems, a triangulation-based algorithm is used. The algorithms are implemented by C++ code. Numerical tests for An et al.’s algorithm are given to show that it runs significantly in terms of run time and memory usage.

Phan Thanh An, Nguyen Ngoc Hai, Tran Van Hoai, Le Hong Trang

Protecting Biometric Features by Periodic Function-Based Transformation and Fuzzy Vault

Abstract

Biometrics-based authentication is playing an attractive and potential approach nowadays. However, the end-users do not feel comfortable to use it once the performance and security are not ensured. Fuzzy vault is one of the most popular methods for biometric template security. It binds a key with the biometric template and obtains the helper data. However, the main problem of fuzzy vault is that it is unable to guarantee the revocability property. In addition, most of the fuzzy vault schemes are performed on two biometrics modalities, fingerprints and iris. In previous works, authors suggested some cancelable transformations attached to a fuzzy vault scheme to overcome these weaknesses. However, the computational cost of these proposals was quite large. In this paper, we present a new hybrid scheme of fuzzy vault and periodic function-based feature transformation for biometric template protection. Our transformation is not only simpler but also suitable for many kinds of biometrics modalities. The newly proposed fuzzy vault scheme guarantees the revocability property with an acceptable error rate.

Thu Thi Bao Le, Tran Khanh Dang, Quynh Chi Truong, Thi Ai Thao Nguyen

EPOBF: Energy Efficient Allocation of Virtual Machines in High Performance Computing Cloud

Abstract

Cloud computing has become more popular in provision of computing resources under virtual machine (VM) abstraction for high performance computing (HPC) users. A HPC cloud is such a cloud computing environment. One of the challenges of energy-efficient resource allocation of VMs in HPC clouds is the trade-off between minimizing total energy consumption of physical machines (PMs) and satisfying Quality of Service (e.g. performance). On the one hand, cloud providers want to maximize their profit by reducing the power cost (e.g. using the smallest number of running PMs). On the other hand, cloud customers (users) want highest performance for their applications. In this paper, we study energy-efficient allocation of VMs that focuses on scenarios where users request short-term resources at fixed start-times and non-interrupted durations. We then propose a new allocation heuristic (namely Energy-aware and Performance-per-watt oriented Best-fit (EPOBF)) that uses performance-per-watt as a metric to choose which most energy-efficient PM for mapping each VM (e.g. the maximum of MIPS/Watt). Using information from Feitelsons Parallel Workload Archive to model HPC jobs, we compare the proposed EPOBF to state-of-the-art heuristics on heterogeneous PMs (each PM has multicore CPUs). Simulations show that the proposed EPOBF can significantly reduce total energy consumption when compared with state-of-the-art allocation heuristics.

Nguyen Quang-Hung, Nam Thoai, Nguyen Thanh Son

Human Object Classification Using Dual Tree Complex Wavelet Transform and Zernike Moment

Abstract

Presence of variety of objects degrade the performance of video surveillance system as a certain type of objects can be misclassified as some other types of object. Recent researches in video surveillance are focused on accurate classification of human objects. Classification of human objects is a crucial problem, as accurate human object classification is a desirable task for better performance of video surveillance system. In this paper we have proposed a method for human object classification, which classify the objects present in a scene into two classes: human and non-human. The proposed method uses combination of Dual tree complex wavelet transform and Zernike moment as feature of object. We have used support vector machine (SVM) as a classifier for classification of objects. The proposed method has been tested on standard dataset like INRIA person dataset. Quantitative experimental results shows that the proposed method is better than other state-of-the-art methods and gives better performance for human object classification.

Manish Khare, Nguyen Thanh Binh, Rajneesh Kumar Srivastava

Erratum to: On the Performance of Triangulation-Based Multiple Shooting Method for 2D Geometric Shortest Path Problems

Phan Thanh An, Nguyen Ngoc Hai, Tran Van Hoai, Le Hong Trang

Backmatter

Titel: Transactions on Large-Scale Data- and Knowledge-Centered Systems XVI
herausgegeben von: Abdelkader Hameurlain
Josef Küng
Roland Wagner
Tran Khanh Dang
Nam Thoai
Verlag: Springer Berlin Heidelberg
Electronic ISBN: 978-3-662-45947-8
Print ISBN: 978-3-662-45946-1
DOI: https://doi.org/10.1007/978-3-662-45947-8