iosr-JCE   Volume-17 ~ Issue-2 ~ March-April 2015

Abstract: Nowadays handwritten character recognition is still remain an open problem because of the variability in writing style. Conversion of handwritten characters is important for making manuscripts into machine recognizable form so that it can be easily accessed and preserved. Many researchers have worked in the area of handwriting recognition and numerous techniques and models have been developed to recognize handwritten text. The study investigates that in any character recognition system there exist three major stages such as Preprocessing, Feature Extraction and Classification. This paper provides a comprehensive review of existing works in offline handwritten character recognition.
Keywords: Back Propagation, Chain Code, Moment invariants, Probabilistic Neural Network, SIFT, Wavelet, Zoning,

[1]. B.V. Dhandra, Shashikala Parameshwarapa and Gururaj Mukarambi, Kannada Handwritten Vowels Recognition based on Normalized Chain Code and Wavelet Filters, International Journal of Computer Applications Recent Advances in Information Technology, NCRAIT - November 4, 2014, 21-24.
[2]. Kanika Bansal and Rajiv Kumar, K Algorithm:-A Modified Technique For Noise Removal In Handwritten Documents, International Journal of Information Sciences & Techniques, Vol. 3 Issue 3, May 2013.
[3]. Suzete E. N. Correia and Joao M de Carvalho, Optimizing the Recognition Rates of Unconstrained Handwritten Numerals Using Biorthogonal Spline Wavelets, Pattern Recognition, 15th International Conference on Barcelona, Published by IEEE, vol.2, 2000, 251-254.
[4]. Ntogas Nikolaos and Ventzas Dimitrios, A Binarization Algorithm For Historical Manuscripts, 12th WSEAS International Conference on Communications, Heraklion, Greece, July 23-25, 2008, 41-51.
[5]. Xian Zhao, Ping Xiao, Wavelet-Based The Character Recognition In MAP, International Conference on Integrated System for Spatial Data Production Commission II, Volume XXXIV, PART 2, Aug.20-23, 2002, 605-608.

Abstract: Technological, Finance, Scientific, engineering and other applications and in specific grand challenge applications are becoming ever more demanding in terms of their computing requirements. Gradually, all these requirements can no longer be managed by a single organization. A Cost-effective modern technology that could address the computing bottleneck is grid computing, which enable clusters of organizations to share their computing ability in an effective way. The resource management system is the main component of any network computing system. There is a lot of work in the hands of Grid computing research community focusing on network computing that have designed and implemented resource management systems with a variety of architectures and services. The discovery of a resource that meets the requirements of a Grid user specific to his query, plays an important role in Grid management system. In today's seamless computing environment effective use of pooled resources is a challenging task. Various strategies that support the Grid system management are reviewed in this paper .The need to polish these strategies and the role of Data mining technologies in this betterment are the key components of this paper. The means to apply Data Mining techniques for better Grid management are identified.
Keywords: Grid computing, Resource Discovery, Grid Management System, Data Mining techniques.

[1]. J. Joseph and C. Fellenstein, "Grid Computing", IBM Press Pearson Education, pp. 5.
[2]. R. Buyya, D. Abramson, J. Giddy, Nimrod/G: An Architecture for a Resource Management and Scheduling System in a Global Computational Grid, International Conference on High Performance Computing in Asia- Pacific Region (HPC Asia 2000), Beijing, China. IEEE Computer Society Press, USA, 2000.
[3]. Chaitanya Kandagatla, "Survey and Taxonomy of Grid Resource Management Systems", University of Texas, Austin
http://www.cs.utexas.edu/users/browne/cs395f2003/project s/KandagatlaReport.pdf
[4]. Klaus Krauter, Rajkumar Buyya and Muthucumaru Maheswaran, "A taxonomy and survey of grid resource management systems for distributed computing",Copyright 2001 John Wiley & Sons, Ltd. 17 September 2001.
[5]. A. Iamnitchi and I. Foster, "On Fully Decentralized Resource Discovery in Grid Environments", IEEE International Workshop on Grid Computing, Denver, CO, 2001.

Abstract: Grid computing provides access to heterogeneous resources which are distributed geographically which makes resource scheduling a complex problem. Hence, scheduling algorithms are necessary which allocate jobs to resources by taking into account the requirements of grid environment. The aim of scheduling is to achieve highest possible throughput by matching the need of the application with the computing resources available. In this paper, we review the various resource scheduling algorithms and discuss their pros and con.
Keywords: Grid Computing, Grid Scheduling, Scheduling Parameters, Scheduling Algorithms.

[1]. N. Malarvizhi and V.R.Uthariaraj, Hierarchical Load Balancing Scheme for Computational Intensive Jobs in Grid Computing
Environment, ICAC, IEEE, 2009
[2]. Grid Computing: Various Job Scheduling Strategies Abhang Swati Ashok Durole Pankaj Hari
[3]. Heuristics in Grid Scheduling, D. Thilagavathi and Dr. Antony SelvadossThanamani
[4]. Raksha Sharma, Vishnu Kant Soni, Manoj Kumar Mishra, PrachetBhuyan "A Survey of Job Scheduling and Resource Management in Grid Computing", 2010.
[5]. Study of an Iterative Technique to Minimize Completion Times of Non-Makespan Machines, by Luis Diego Briceno, Mohana
Oltikar, Howard Jay Siegel, and Anthony A. Maciejewski, 2007

Abstract: The work is to show how simply a data can be hidden inside an image and be easily extracted using Matlab. The work focuses on bit plane extraction and how it can be used to complete the above mentioned task. Both encryption and decryption procedure has been discussed. Here the data to be hidden refers to any piece of writing that may contain valuable information. It does not include any audio or video file. The main focus has been given to grayscaled images, which acts as the reference image that covers the data.
Keywords: Bit plane slicing, Decryption, Encryption, Histogram, Steganography.

[1]. H. Faheem Ahmed and U. Rizwan, Embedding Multiple Images in an Image Using Bit Plane, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 1, January 2013, ISSN: 2277 128X
[2]. N S T Sai and R C Patil, IMAGE RETRIEVAL USING BIT-PLANE PIXEL DISTRIBUTION, International Journal of Computer Science & Information Technology (IJCSIT), Vol 3, No 3, June 2011
[3]. Morphological Image Processing, Available at: https://www.cs.auckland.ac.nz/courses/compsci773s1c/lectures/ImageProcessing-html/topic4.htm#morpho
[4]. Intensity Histogram, Available at: http://homepages.inf.ed.ac.uk/rbf/HIPR2/histgram.htm
[5]. A.M.Raid, W.M.Khedr, M.A.El-dosuky and Mona Aoud, IMAGE RESTORATION BASED ON MORPHOLOGICAL.

Abstract:The widespread adoption of cyberspace for exceptional socio-economic activities, especially as it is connecting populations around the globe in ways never foreseen is raising fresh security issues. What is fuelling this embracement, is the pervasiveness of social media and innovative mobile computing devices. This has not only changed our ways of life, but also blurs the lines that define the way governments run, business are conducted as well as the way we use and share information. Yet, these new ways of services and interactions, are raising new threats in terms of privacy, integrity, confidentiality and trust. Cyberspace transactionscut across national boundaries, in many cases, without any form of existing trust relationships of any sort.

[1]. Rodge Clark (2006), what is Privacy [online], Available from: http://www.rogerclarke.com/DV/Privacy.html [Accessed: 15th December, 2014]
[2]. OCED, (2014), Privacy Principles [online], Available from:http://oecdprivacy.org/ [Accessed: 15th December, 2014].
[3]. Rodge Clark (2001), Biometrics and Privacy, Available from:http://www.rogerclarke.com/DV/Biometrics.html [Accessed: 12th, December, 2014]
[4]. A. Acquisti and J. Grossklags, (2005) Privacy and Rationality in Individual Decision Making," IEEE Security and Privacy, vol. 3, pp. 26-33.
[5]. EU, (2002) Directive 2002/58/EC on Privacy and Electronic Communications, European Parliament and the Council
[6]. E. Bertino, E.Ferrari, and A. Squicciarini, (2004), Trust Negotiations: Concepts, Systems and Languages, IEEE Computer, pp. 27-34,
[7]. U. M. Mbanaso, G. S. Cooper, D. Chadwick, and A. Anderson, (2007) Obligations for Privacy and Confidentiality in Distributed Transactions, presented at Emerging Directions in Embedded and Ubiquitous Computing, Dec 2007, pp 69-81

Abstract: Semantic Similarity is a concept whereby the set of documents are measured to find the likeliness of their meaning content. Document Similarity is the process of Computing the Semantic Similarity between Multiple Documents Using Similarity measures. In this paper, the document similarity has been applied to compute the pair wise similarities of documents based on the Suffix Tree Document (STD) model. Documents are pre-processed initially. Data Preprocessing can be done to increase the efficiency of the Similarity values. The pre-processed phrases are inserted in Suffix tree. A Suffix tree is a data structure that presents the suffixes of a given string in a way that allows for a particularly fast implementation of much important string operation. The suffix substrings are selected as the phrases to label the edges of the suffix tree. Internal nodes represents phrases that shared by Multiple Documents. The similarity of two documents can be defined as the more internal nodes shared by the two documents. Suffix tree can be used to solve the exact matching problem in linear time. Document similarity naturally inherits the term tf-idf(Term frequency and inverse Document frequency) weighting scheme in computing the document similarity with phrases. Tf-Idf method has been used to calculate the weight of Internal nodes of the suffix tree, where internal nodes are the nodes that has been shared by multiple documents. Cosine, Dice and Hellinger measures applied to find the pair wise similarity based on the weight of each internal node of the suffix tree.

Keywords: Semantic similarity, Similarity measures, Document similarity, Suffix tree and Tf-idf scheme.

[1]. Hung Chim and Xiaotie Deng, "Efficient Phrase-Based Document Similarity for Clustering" IEEE Transactions On Knowledge
And Data Engineering, Vol. 20, No. 9, pp. 1217-1229, 2008.
[2]. Elias Iosif, Alexandros Potamianos, "Unsupervised Semantic Similarity Computation between Terms Using Web Documents"
IEEE Transactions On Knowledge And Data Engineering, Vol. 22, No. 11, pp: 1637-1647, 2010.
[3]. Angelos Hliaoutakis, et al. , "Information Retrieval by Semantic Similarity" , in. International Journal on Semantic Web &
Information Systems, Vol.2, No.3, pp.55-73, 2006.
[4]. Giannis Varelas et al., "Semantic Similarity Methods in WordNet and their Application to Information Retrieval on the Web",
Proc. of the 7th ACM International workshop on Web information and Data Management , pp. 10 -16, 2005.
[5]. Francine Chen, Ayman Farahat, Thorsten Brants, "Multiple Similarity Measures and Source-Pair Information in Story Link
Detection", Proc. of Human Language Technology Conference, pp. 313-320, Chicago, 2004.

Abstract: Most machine learning techniques employed in the area of text classification require the features of the documents to be effectively selected owing to the large chunk of data encountered in the classification process and term weights built from document vectors for proper infusing into the respective classifier algorithms. Effective selection of the most important features from the raw documents is achieved by implementing more extensive pre-processing techniques and the features obtained were ranked using the chi-square statistical approach for the elimination of irrelevant features and proper selection of more relevant features in the entire corpus. The most relevant ranked features obtained are converted to word vectors which is based on the number of occurrences of words in the documents or categories concerned, using the probabilistic characteristics of Naïve Bayes as a vectorizer for machine learning classifiers. This hybrid vector space model was experimented on legal text categories and the study revealed better discovered features using the pre-processing and ranking technique, while better term weights from the documents was successfully built for machine learning classifiers used in the text classification process.

Keywords – Chi-square statistics, Feature selection, Feature ranking, Pre-processing, Stemming, Vectorization

[1]. M. Duoqian, D. Qiguo, Z. Hongyun and, J. Na, Rough Set Based Hybrid Algorithm for Text Classification, Proceedings from Journal of Expert Systems with Applications, 36(5), 2009, 9168 – 9174.
[2]. G. Forman, An Extensive Empirical Study of Feature Selection Metrics for Text Classification, The Journal of Machine Learning. Res. 3., 2003, 1289 – 1305.
[3]. Y. Yang, Noise Reduction in a Statistical Approach to Text Categorization, Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 1995, 256 – 263.
[4]. Y. Yang and J.O Pedersen, A Comparative Study of Feature Selection in Text Categorization, Proceedings of the 14th International Conference on Machine Learning, 1997, 412 – 420.
[5]. D. Mladeni and M. Grobelnik, Feature Selection on Hierarchy of Web Documents, Journal of Decision Support Systems, 35, 2003, 45 -87.
[6]. H. Liu, J. Sun, L. Liu and H. Zhang, Feature Selection with Dynamic Mutual Information, Journal of Pattern Recognition, 42, 2009, 1330 – 1339.

Abstract: Distributed software engineering is one of the hot research areas that has the most interest within the software industry for many IT organizations especially the global and multinational ones. Up to 90% of these organizations running projects are distributed over different teams and even countries, which puts Global Software Engineering (GSE) with high value from both industrial and academic perspectives. Many challenges attack these organizations due to current economical slow down makes delivery efficiency, cost control and time to market key Critical Success Factors (CSF) to survive in these market conditions. DevOps is newly emerging metrology stresses communication, collaboration and integration between software developers and technical operations team to rapidly deliver software products and services to the market. The value behind DevOps is two folds: first one is mitigating the challenges faced by distributed software engineering and second is to bridge any gaps within current traditional organization processes. This paper introduces a new DevOps maturity model and assesses how this model will impact existing GSE practices and processes.

Keywords: Critical Success Factors - Collaboration -Continuous delivery - Continuous deployment – Continuous Integration Distributed development – DevOps - Governance - Global Software Engineering - IT operations – Quality.

[1]. Juristo, N., Moreno, A.M., and Silva, A.A. 2002. Is the European Solving Industry Moving Toward Requirements Engineering Problems? IEEE Software 19(6): 70-77
[2]. Komi-Sirviö, S, and Tihinen, M. 2003. Great Challenges and Opportunities of Distributed Software Development - An Industrial Survey. In proceedings of the 15th International Conference on Software Engineering and Knowledge Engineering, SEKE2003, San Francisco, USA pp. 489 – 496
[3]. Jez Humble, Chris Read, Dan North, The Deployment Production Line, Proceedings of Agile 2006, IEEE Computer Society
[4]. da Silva, F.Q.B., Costa, C., Frana, A.C.C. & Prikladinicki, R. 2010. Challenges and Brazil, solutions in distributed software development project management: A systematic literature review. In: Global Software Engineering (ICGSE) 2010 5th IEEE International Conference on Global Software Engineering, Recife, pp. 87–96

Abstract: Cloud computing is evolving as a key computing platform for sharing resources that include infrastructures, software, applications, and business. An increasing number of companies are expected to migrate their applications to cloud environment. So when planning to move a legacy style application to the cloud various challenges arise. The potential size and complexity of such a project might especially discourage small or medium companies trying to benefit from the advantages the cloud promises. By analyzing the research achievements and application status, we divide the existing migration methods into three strategies according to the cloud service models integrally. Different processes need to be considered for different migration strategies, and different tasks will be involved accordingly. Moreover, we have also observed that there is hardly any guidance available for migrating existing systems to cloud computing in terms of software engineering aspects. In this paper, we propose an architecture that describes the cloud migration process, starting by understand application architecture, Choice of type of cloud environment and Identification and categorization of the various types of application migration to the Cloud and solutions for migrating architectural components.

Keywords: Cloud Computing, Cloud Migration Process, Meta Model, Software Migration

[1] Armbrust, M., et al.: Above the Clouds: A Berkeley View of Cloud Computing. Tech. Rep. UCB/EECS-2009-28, EECS Department, University of California, Berkeley (2009).
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[4] G. Gruman, E. Knorr. What cloud computing really means, [Online], Available: http://www.infoworld.com , October 18,2014
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Abstract: This paper discusses how a company can implement multicast communication system making use of its existing data network to offer free TV channels. The advantages and challenges involved in multicast communications system and the design, configurations and deployment of a prototype multicast communications network while implementing an appropriate multicast routing protocol are discussed. Keyword: Multicast, High availability, PIM, IGMP

[1]. B. Cain, S. Deering, I. Kouvelas, B. Fenner, A Thyagarajan, (2002). Internet Group Management Protocol, Version 3, , RFC 4604.
[2]. Minoli, D. (2008). IP Multicast with Applications to IPTV and Mobile DVB-H. Hoboken: John Wiley and Sons.
[3]. Paul, S. (2011). Digital Video Distribution in Broadband, Television, Mobile and Converged Networks: Trends, Challenges and Solutions. West Sussex: John Wiley and Sons.
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[5]. Simpson, W. (2008) Video Over IP: IPTV, Internet Video, H.264, P2P, Web TV, and Streaming: A Complete Guide to Understanding the Technology. 2nd ed. Oxford: Elsevier Inc.
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Abstract: This paper presents a hybrid approach to face classification and detection. The remarkable advancement in technology has enhanced the use of more accurate and precise methods to identify and recognize things. Face detection and identification is a new field because face is undeniably related to its owner except in case of identical twins. This paper presents a combination of three well known algorithms Viola- Jones face detection framework, Neural Networks method to detect faces in static images. Face recognition is a kind of biometric detection using the physiological measure to identify and detect face. It allows the user to passively identify the person and its features using biometric. The proposed work emphasizes on the face detection and identification using Viola-Jones algorithm which is a real time face detection system. Neural Networks will be used as a classifier between faces and non-faces.

Keywords: Face detection and identification; Viola-Jones algorithm; feature vectors; Integral images; Adaboost.

[1]. Yang M.H., Kriegman D., and Ahuja N., "Detecting Faces in Images: A Survey", IEEE Trans. Pattern Analysis Machine Intelligence, 24(1), 2002.
[2]. Yunawen Wu, and Xueyi Ai, "Face Detection in Color Images Using AdaBoost Algorithm Based on Skin Color Information", First International Workshop on Knowledge Discovery and Data Mining, 2008, pp.339-342
[3]. Kwok-Wai Wong, Kin-Man Lam, and Wan-Chi Siu, "An Efficient Algorithm for Human Face Detection and Facial Feature Extraction under Different Conditions", Pattern Recognition, Vol. 34,2001, pp.1993-2005.
[4]. Ing-Sheen Hsieh, Kuo-Chin Fan, and Chiunhsiun Lin, "A Statistic Approach to the Detection of Human Faces in Color Nature Scene", Pattern Recognition, 35, 2002, pp.1583-1596.
[5]. J. G. Wang and T. N. Tan, "A New Face Detection Method Based on Shape Information", IEEE Transactions on Pattern Recognition Letters, Vol. 21, 2000, pp. 463-471.
[6]. Chiunhsiun Lin and Kuo-Chin Fan, "Triangle-based Approach to the Detection of Human face", Pattern Recognition, Vol. 34, No. 6, 2001, pp.1271-1283.

Abstract: This paper presents a detailed review of Offline Handwritten Character Recognition. HCR is an optical character recognition, which convert the human readable character to machine readable format. In HCR, to attain 99% accuracy is very difficult. Here a detailed study on Geometrical methods of feature extraction in character recognition has been done by giving more emphasis to Zone based techniques and it has been analyzed that the efficiency of HCR depends on the selection of appropriate feature extraction methods and classifier. A comparative study in various steps in character recognition like Preprocessing, Segmentation, Feature Extraction and Classification are carried out. Various application areas of HCR like Postal address reading, mail sorting, office automation for text entry, person identification, signature verification, bank-check processing etc. are also analyzed.

Keywords: OCR, Preprocessing, Segmentation, Feature Extraction, Classification.

[1]. J.Pradeep, E.Srinivasan and S.Himavathi, Diagonal Feature Extraction Based Handwritten Character System Using Neural Network, International Journal of Computer Applications (0975-8887), vol.8, no.9, pp.17-22, October 2010.
[2]. S.V.Rajashekararadhya and P.Vanaja Ranjan, Handwritten Numeral/Mixed Numerals Recognition of South-Indian Scripts: The Zone Based Feature Extraction Method, Journal of Theoretical and Applied Information Technology, vol.7, no.1, pp. 063-079, 2005 - 2009.
[3]. S.L.Mhetre and M.M.Patil, A Comparative Study of Two Methods for Handwitten Devanagari Numeral Recognition, IOSR Journal of Computer Engineering, vol.15, pp. 49-53, Nov-Dec.2013.
[4]. S.V. Rajashekararadhya and P. Vanaja Ranjan, Efficient Zone Based Feature Extraction Algorithm for Handwritten Numeral Recognition of Four Popular South Indian Scripts, Journal of Theoretical and Applied Information Technology, pp. 1171-1181, 2005 - 2008.
[5]. S.V. Rajashekararadhya and P. Vanaja Ranjan, Handwritten numeral recognition of Kannada script, Proceedings of the International Workshop on Machine Intelligence Research, pp. 80-86, 2009.

Abstract: Unlike wired networks or other wireless networks, where the nodes communicate with each other via an access point or a base station, wireless Mobile ad hoc network is an infrastructure less network where the network is formed out of randomly moving nodes. The nodes in a Mobile ad hoc network work as a host as well as a router. Since there is no centralized management and the network is formed only by the participating node's cooperation, Mobile ad hoc network (MANET) faces numerous security challenges. Out of many security attacks Blackhole attack is one of the severe one. A Blackhole attack is a Denial-of-service attack. In a request response based route discovery method, a Blackhole node advertises itself as a node that has the shortest route to the destination. It can drop all the packets that it is supposed to relay to the destination. In this paper, we evaluate the performance of one reactive protocol (AODV), one proactive protocol (OLSR) and a hybrid protocol (ZRP).

[1]. Esmaili H.A., KhalijiShoja M. R, Gharaee "Performance Analysis of AODV under Black Hole attack through use of OPNET simulator", World of Computer Science and Information Technology Journal (WCSIT), ,ISSN: 2221-0741,Vol. 1, No. 2, 49-52, 2011
[2]. Ullah I.,Rehman S. "Analysis of Black Hole Attack on MANETs Using Different MANET Routing Protocols", Master Thesis, Electrical Engineering, Thesis no: MEE 10:62,June, 2010
[3]. Das R. Dr. Purkayastha B.S., Dr. Das P. "Security Measures for Black Hole Attack in MANET: An Approach" International Journal of Engineering Science and Technology (IJEST), ISSN : 0975-5462 Vol. 3 No. 4 Apr 2011
[4]. Chowdhury A.,Kunal "Performance Evaluation of AODV under Blackhole Attack", International Journal of Emerging Technology and Advanced Engineering (IJETAE), ISSN 2250-2459,Volume 2, Issue 5, May 2012
[5]. Singh H., Singh G., Singh M., "Performance Evaluation of Mobile Ad Hoc Network Routing Protocols under Black Hole Attack" , International Journal of Computer Applications (0975 – 8887), Volume 42– No.18, March 2012

Abstract: Nowadays, the World Wide Web is the collection of large amount of information which is increasing day by day. For this increasing amount of information, there is a need for efficient and effective index structure. The main aim of search engines is to provide most relevant documents to the users in minimum possible time. This paper proposes the indexing structure in which index is built on the basis of context of the documents rather than on the terms basis using ontology. The context of the document that are being collected by the crawler is extracted using the context repository, thesaurus and ontology repository and then documents are indexed according to their respective context.

Keywords: Context, Context repository, Indexing, Ontology repository, Semantic web.

[1]. Nidhityagi, Rahul Rishi ,R.P. Agarwal "Context based Web Indexing for Storage of Relevant Web Pages" International Journal of Computer Applications (0975 – 8887) Volume 40– No.3, February 2012
[2]. Parul Gupta and A.K.Sharma "Context based Indexing in Search Engines using Ontology", International Journal of Computer Applications, Volume 1 No. 14, pp 49-52, 2010.
[3]. Changshang Zhou, Wei Ding and Na Yang, "Double Indexing Mechanism of Search Engine based on Campus Net", Proceedings of the 2006 IEEE Asia-Pacific Conference on Services Computing (APSCC'06), 2006.
[4]. NareshChauhan and A. K. Sharma," Design of an Agent Based Context Driven Focused Crawler",BVICAM'S International Journal of Information Technology, pp 61-66, 2008.
[5]. Sajendra Kumar, Ram Kumar Rana ,Pawan Singh " Ontology based Semantic Indexing Approach for Information Retrieval System" International Journal of Computer Applications (0975 – 8887) Volume 49– No.12, July 2012.
[6]. B.Chandrasekaran and John R.Josephson, Ohio State University V.RichardBenjamins,Universityof Amsterdam "What are Ontologies,and Why do we need them?"IEEE INTELLIGENT SYSTEMS(1094-7167),Volume 14 No.1,pp20-26,1999.
[7]. S. Chakrabarti , M. van den Berg, and B. Dom. "Focused crawling: a new approach to topic-specific web resource discovery". In WWW-8, 1999.