IOSR Journal of Computer Engineering (IOSR-JCE)

Volume 2 - Issue 6

Paper Type : Research Paper
Title : Comparison for Image Edge Detection Algorithms
Country : Iran
Authors : Li Bin, Mehdi Samiei yeganeh
: 10.9790/0661-0260104       logo
Abstract: Edge is the basic characteristic of image, edge detection plays an important role in computer vision and image analysis. The pretty usefull and identical information contained in edge of sub-image enable edge detection to be the main approach to image analysis and recognition. This paper compares and analyzes several kinds of classical algorithms of image edge detection, including Roberts, Sobel, Prewitt, LOG and Canny with MATLAB tool.
Keywords – Canny, LOG, Prewitt, Roberts, Sobel
Journal Papers:
[1] L.P. Han and W.B. Yin. An Effective Adaptive Filter Scale Adjustment Edge Detection Method(China, Tsinghua university, 1997).
[2] D. Marr and E. Hildreth, Theory of Edge Detection(London, 1980).
[3] Q.H Zhang, S Gao, and T.D Bui, Edge detection models, Lecture Notes in Computer Science, 32(4), 2005, 133-140.
[4] D.H Lim, Robust Edge Detection In Noisy Images, Computational Statistics & Data Analysis, 96(3), 2006, 803-812.
[5] Abbasi TA, Abbasi MU, A novel FPGA-based architecture for Sobel edge detection operator, International Journal of Electronics, 13(9), 2007, 889-896.
[6] Canny John, A Computational Approach to Edge Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(6), 1986, 679-6987
[7] X.L Xu, Application of Matlab In Digital Image Processing, Modern Computer, 43(5), 2008, 35-37.
[8] Y.Q Lv and G.Y Zeng , Detection Algorithm of Picture Edge, TAIYUANSCIENCE & TECHNOLOGY, 27(2), 2009, 34-35
[9] D.F Zhang, MATLAB Digital Image Processing(Beijing, Mechanical Industry, 2009)

Paper Type : Research Paper
Title : Reusing Run-Time Type Identification (RTTI) Generated By Compiler For Construction Of The Class Hierarchy Graph (CHG).
Country : India
Authors : Sajad Ahmad Bhat
: 10.9790/0661-0260512       logo
Abstract : In statically-typed object-oriented languages optimization plays an important role to make program compilation faster. A number of methods have been used, and a variety of algorithms have been suggested and designed for optimization of statically typed object-oriented languages like C++ and Java. One of the important considerations inoptimization of statically typed object-oriented language is function de-virtualization. Function de-virtualization converts virtual function calls to direct calls, i.e. a virtual function call is replaced by a call to the method of some class. The direct calls can be further inlined to enhance the performance. Class hierarchy Analysis (CHA) [3] is the well-known technique used to identify the virtual calls in a program that can be converted into direct calls. The class hierarchy Analysis starts with a building a Class Hierarchy Graph (CHG) that represents the relation between the various classes of program and the visible methods within these classes. In fact the most basic data structure for developing optimization algorithms is the CHG, which abstracts the Base-derived class relationship that use virtual functions.
A number of algorithms have been designed for constructingClass Hierarchy Graph (CHG) like the one designed by Bacon, D.F[1]that uses the source level information to build CHG. Several alternatives have been presented to this approach as well. In this article we will present the method for construction of CHG by reusing the RTTI (RuntimeType Identification) generated by the complier.
[1]. David F. Bacon and Peter F. Sweeney "Fast and Static Analysis of C++ Virtual Function Calls",object oriented programming systems, languages, and applications (OOPSLA) 1996. [2]. UrsHolzle and GerladAigner "Eliminating Virtual Function Calls in C++ Programs"Conference Proceedings, Springer Verlag LNCS 1098, pp. 142-166 ECOOP 1996. [3]. Grove, Chambers. And Dean, J., D. "Optimization of Object-Oriented Programs Using Static Class Hierarchy Analysis" Tech Report, Dept. of CSE, University of Washington, 1994.
[4]. David Bernstein, YaroslavFedorov, Sara Porat, Joseph Rodrigue, and EranYahav.Compiler Optimization of C++ Virtual Function Calls. 2nd Conference on Object-Oriented Technologies and Systems, Toronto, Canada, June 1996. [5]. David F. Bacon, [Fast and Effective Optimization of Statically Typed Object-Oriented Languages] Ph.D. Thesis, University of California Berkeley, 1997. [6]. Wu, P.-C. And Wang, F.-J. 1996. On efficiency and optimization of C++ programs. Softw. Pract. Exper., 26, 4 (Apr.), 453{465. [7]. Porat, S., Bernstein, D., Fedorov, Y., Rodrigue, J., and Yahav, E. 1996. [Compiler optimizations of C++ virtual function calls]. In Proceedings of the Second Conference on Object-Oriented Technologies and Systems, (Toronto, Canada, June). Usenix Association, pp. 3{14.
[8]. Bacon, D. F., Graham, S. L., and Sharp, O. J. 1994. Compiler transformations for high-performance computing. ACM Comput. Surv., 26, 4 (Dec.), 345{420.
[9]. Pande, H. D. and Ryder, B. G. 1995. Static type determination and aliasing for C++. Tech. Rep. LCSR-TR-250, Dept. of Computer Science, Rutgers University, (July).
[10]. Pande, H. D. and Ryder, B. G. 1996. Data-flow-based virtual function resolution. In Proceedings of the Third International Static Analysis Symposium, volume 1145 of Lecture Notes in Computer Science. Springer-Verlag, Berlin, Germany, pp. 238{254.
[11]. Nackman, L. R. and Barton, J. J. 1994. Base-Class Composition with Multiple Derivation and Virtual Bases. In Proceedings of the 1994 USENIX C++ Conference, (Cambridge, Mass., Apr.). Usenix Association, Berkeley, Calif., pp. 57{71.
[12]. Calder, B. and Grunwald, D. 1994. Reducing indirect function call overhead in C++ pro-grams. In Conference Record of the Twenty-First ACM Symposium on Principles of Programming Languages, (Portland, Ore., Jan.). ACM Press, New York, N.Y., pp. 397{408.
[13]. Dean, J., Grove, D., and Chambers, C. 1995. [Optimization of object-oriented programs using static class hierarchy analysis]. In Olthoff, W., Ed., Proceedings of the Ninth European Conference on Object-Oriented Programming { ECOOP'95, volume 952 of Lecture Notes in Computer Science, (Aarhus, Denmark, Aug.). Springer-Verlag, Berlin, Germany, pp. 77{101.
[14]. Ellis, M. and Stroustroup, B. 1990. [The Annotated C++ Reference Manual]. Addison-Wesley, Reading, Mass.

Paper Type : Research Paper
Title : Statistical Framework For Load Balancing In Grid Computing For Efficient Job Migration
Country : India
Authors : Ashish Chandra Srivastava, A Kakoli Rao
: 10.9790/0661-0261319       logo
Abstrac: The proposed system has addressed issues that are imperative to Grid computing environments by introducing job migration algorithms. The proposed algorithms differ within the manner load balancing is disbursed and is shown to be cost effective in minimizing the response time on Grid environments. The algorithm is enhanced for large-scale systems to take into account the job migration value, resource heterogeneity, and network heterogeneity when load balancing is considered. The algorithm is applicable to small-scale systems, performs load balancing by estimating the expected end time of employment on individual processors on every job arrival to estimate system parameters like the job arrival rate, processing rate, and load on the processor and balance the load by migrating jobs to individual processors by considering job transfer value, resource heterogeneity, and network heterogeneity.
Keywords-component; Grid Computing, Load balancing, Inter arrival time, processing elements
[1] Nadia Ranaldo, Giancarlo Tretola, and Eugenio Zimeo ―A Scheduler for a Multi-paradigm Grid Environment‖ INRIA Sophia-Antipolis - Universit´ de Nice - CNRS/I3Se 2004, Route des Lucioles, BP 93 FR-06902 Sophia Antipolis, France
[2] Mrs. Sharada Patil, Prof. Dr. Arpita Gopal ―Comparison of Cluster Scheduling Mechanism using Workload and System Parameters‖ International Journal of Computer Science and Application ISSN: 0974-0767
[3] Malarvizhi Nandagopal Rhymend V Uthariaraj and ―Hierarchical Status Information Exchange Scheduling and Load Balancing For Computational Grid Environments Balancing For Computational Grid Environments‖ IJCSNS International Journal of Computer Science and Network Security, VOL.10 No.2, February 2010
[4] Fangpeng Dong and Selim G. Ak ―Scheduling Algorithms for Grid Computing: State of the Art and Open Problems‖ International Conference on e-Science and Grid Computing
[4] Malarvizhi Nandagopal, Rhymend V. Uthariaraj ―Hierarchical Load Balancing Approach in Computational Grid Environment‖ International J. of Recent Trends in Engineering and Technology, Vol. 3, No. 1, May 2010
[5] P.Neelakantan ―DECENTRALIZED LOAD BALANCING IN HETEROGENEOUS SYSTEMS USING DIFFUSION APPROACH‖ International Journal of Distributed and Parallel Systems (IJDPS) Vol.3, No.1, January 2012
[6] Hongzhang Shan, Leonid Oliker, Warren Smith, Rupak Biswas ―Scheduling in Heterogeneous Grid Environments: The Effects of Data Migration‖ International Conference on Advanced Computing and Communication, Ahmedabad
[7] Satish Penmatsa, Anthony T. Chronopoulos ―Comparison of Price-based Static and Dynamic Job Allocation Schemes for Grid Computing Systems‖ 2009 Eighth IEEE International Symposium on Network Computing and Applications
[8] Jeremy K. Chen Theodore S. Rappaport Gustavo de Veciana ―Iterative Water-filling for Load-balancing in Wireless LAN or Microcellular Networks‖ Vehicular Technology Conference, 2006. VTC 2006-Spring. IEEE 63rd, Volume: 1 Page(s): 117 - 121
[9] Giuseppe Di Fatta , Michael R. Berthold ―Decentralized Load Balancing for Highly Irregular Search Problems‖ Microprocessors & Microsystems,Volume31Issue4,June,2007 Pages 273-281
[10] Lorenzo Muttoni, Giuliano Casale, Federico Granata, Stefano Zanero ―Optimal number of nodes for computation in grid environments‖ Dipt. di Elettronica ed Informazione, Politecnico di Milano, Italy Page(s): 282 – 289, 11-13 Feb. 2004

Paper Type : Research Paper
Title : Survey on Random Early Detection Mechanism and Its Variants
Country : Egypt
Authors : Alshimaa H. Ismail, Zeiad Elsagheer, I. Z. Morsi
: 10.9790/0661-0262024       logo
Abstract: Active Queue Management (AQM) algorithms are an Implementing schemes, So that packets are transmitted with higher priority than others. Random Early Detection (RED) is the first active queue management algorithm proposed for deployment in TCP/IP networks. RED has some parameter tuning issues that need to be carefully addressed for it to give good performance under different network scenarios. Various algorithms come from RED such as Stabilized RED (SRED), Dynamic RED (DRED), Adaptive RED (ARED) and Flow RED (FRED) these algorithms control congestion by discarding packets with a load dependent probability whenever a queue in the network appear to be congested, this paper will introduced some features about RED and its variants. Keywords: Active Queue Management, Congestion control, Queue size, RED, TCP/IP
[1] Agrawal R. and Srikant R. (2000). Privacy preserving data mining, In Proc. of the ACM SIGMOD Conference on Management of Data, Dallas, Texas, 439-450.
[2] Berners-Lee J, Hendler J, Lassila O (2001) The Semantic Web. Scientific American, vol. 184, pp34-43.
[3] Berendt B., Bamshad M, Spiliopoulou M., and Wiltshire J. (2001). Measuring the accuracy of sessionizers for web usage analysis, In Workshop on Web Mining, at the First SIAM International Conference on Data Mining, 7-14.
[4] Srivastava, et aI. , Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data. SIGKDD Explorations, 1(2) 2000,p. 12-23 (3).
[5] R. Kosala and H. Blockeel. Web Mining Research: A Survey, ACM SIGKDD Explorations Newsletter, June 2000, Volume 2 Issue 1.
[6] Kosala, R., and Blockeel, H., (2000). Web Mining Research: A Survey, ACM 2(1):1-15.
[7] Brin, S., and Page, L. (1998). The Anatomy of a Large- Scale Hypertextual Web Search Engine, Proceedings of the 7th International World Wide Web Conference, Elsevier Science, New York, 107-117.
[8] Desikan, P., Srivastava, J., Kumar, V., and Tan, P.N. (2002). Hyperlink Analysis: Techniques and Applications, Technical Report (TR 2002-0152), Army High Performance Computing Center.
[9] Li Haigang Yin wanling "Study of Application of Web Mining Techniques in E-Business" IEEE Conference , 2006
[10] B. Mobasher, H. Dai, T. Luo, M. Nakagawa. Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization. In Data Mining and Knowledge Discovery, Kluwer Publishing, Vol. 6, No. I, pp. 61-82, January 2002.

Paper Type : Research Paper
Title : Multitude Regional Texture Extraction for Image Segmentation of Aerial and Natural Images
Country : India
Authors : S. Rizwana
: 10.9790/0661-0262530       logo
Abstract: Image processing plays a major role in evaluation of images in many concerns. Manual interpretation of the image is time consuming process and it is susceptible to human errors. Computer assisted approaches for analyzing the images have increased in latest evolution of image processing. Also it has highlighted its performance more in the field of medical sciences. Many techniques are available for the involvement in processing of images, evaluation, extraction etc. The main goal of image segmentation is cluster pixeling the regions corresponding to individual surfaces, objects, or natural parts of objects and to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. The proposed method is to conquer segmentation and texture extraction with Regional and Multitude and techniques involved in it. First for aerial and natural imaging we present region based segmentation. Homogeneous regions depend on image granularity features. Second a local threshold based multitude texture regional seed segmentation for Aerial and natural image segmentation is proposed. Here extraction is done with dimensions comparable to the speckle size are to be extracted. The algorithm provides a less natural metrics awareness in a minimum user interaction environment. The shape and size of the growing regions depend on look up table entries. The experimental evaluation is conducted with training samples of natural and aerial images to show the performance of multitude textural extraction for more efficient image segmentation with sharp demarcation of edge portions along with intensity levels.
Key Words: Image Segmentation, Texture Extraction, Region Growing, Natural and Aerial Images
[1] T.-Y. Law and P. A. Heng, (2000) "Automated extraction of bronchus from 3-D CT images of lung based on genetic algorithm and 3-D region growing", Proc. SPIE 3979, Medical Imaging 2000: Image Processing, 906-916.
[2] R. Susomboon, D. S. Raicu, and J. D. Furst, (2006)"Pixel-Based Texture Classification of Tissues in Computed Tomography", CTI Research Symposium, Chicago, April 2006.
[3] J. E. Koss, F. D. Newman, T. K. Johnson, D. L. Kirch, (1999) "Abdominal organ segmentation using texture transform and a Hopfield neural network", IEEE Trans. Medical Imaging, Vol.18, 640-648.
[4] R. Adams, L Bischof, (1994) "Seeded region growing". IEEE Transaction Pattern Analysis Machine Intelligency 16, 641-647.
[5] N. A. Mat-Isa, M. Y. Mashor & N. H. Othman, (2005) "Seeded Region Growing Features Extraction Algorithm; Its Potential Use in Improving Screening for Cervical Cancer". International Journal of the Computer, the Internet and Management (ISSN No: 0858-7027) Vol. 13. No. 1 January-April
[6] Y. Tuduki, K. Murase, M. Izumida, H. Miki, K. Kikuchi, K. Murakami & J. Ikezoe (2000). "Automated Seeded Region Growing Algorithm for Extraction of Cerebral Blood Vessels from Magnetic Resonance Angiographic Data" Proceedings of The 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society 1756-1759
[7] P. A. Venkatachalam, U. K.Ngah, A. F. M. Hani& A. Y. M. Shakaff, (2002). "Seed Based Region Growing Technique in Breast Cancer Detection and Embedded Expert System". Proceedings of International Conference on Artificial Intelligence in Engineering and Technology 464-469
[8] V. A. Kovalev, F. Kruggel, H.-J Gertz, and D.Y. von Cramon. (2001) "Three-dimensional texture analysis of MRI brain datasets" IEEE Trans. on Medical Imaging, 20(5): 424-433.
[9] S. A. Karkanis, et al., (1999) "Detecting abnormalities in colonoscopic images by texture descriptors and neural networks," Proc. of the Workshop Machine Learning in Med. App., 59-62.
[10] A. Madabhushi, M. Feldman, D. Metaxas, D. Chute, and J. Tomaszewski. (2003) "A novel stochastic combination of 3D texture features for automated segmentation of prostatic adenocarcinoma from high resolution MRI." Medical Image Computing and Computer-Assisted Intervention, volume 2878 of Lecture Notes in 8 J. Wu et al. / J. Biomedical Science and Engineering 2 (2009) 1-8 Computer Science, pp. 581-591. Springer-Verlag
[11] B. W. Whitney, N. J. Backman, J. D. Furst, D. S. Raicu, (2006) "Single click volumetric segmentation of abdominal organs in Computed Tomography images", Proceedings of SPIE Medical Imaging Conference, San Diego, CA, Februar.
[12] J. Wu, S. Poehlman, M. D. Noseworthy, M. Kamath, (2008) Texture Feature based Automated Seeded Region Growing in Abdominal MRI Segmentation, 2008 International Conference on Biomedical Engineering and Informatics, Sanya, China, May 27-30.

Paper Type : Research Paper
Title : Find a Text in Image File Using Correlation Method
Country : Iraq
Authors : Ziad M. Abood, Intisar Abd Yousif, Ahmed Kawther Hussein
: 10.9790/0661-0263135       logo
Abstract: Image correlation is representative of a wide variety of window-based image processing tasks. We can be search for text/ word in documents with extension as DOC, PDF, TXT … etc., and count the number of it in document or in the page. the difficulty in this process when we are keeping this documents in the form of images and any extension such as(jpg, bmp, … etc.), thus lead to difficulty in the search for a word in those images, as some peoples depend on convert the document to image file in order to keep document from manipulation or making copies of them. This study introduces method by using V.B. and depends on correlation method in search for "word" or "symbol" in image files.
Index Terms- image correlation, image processing, V.B, search.
[1] R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis. New York: Wiley, 1973.
[2] A. Rosenfeld and A. C. Kak, Digital Picture Processing. New York: Academic, 1976.
[3] W. K. Pratt, Correlation techniques of image registration, IEEE Trans. Aerosp. Electron. Syst., vol. AES-10, pp. 353-358, May 1974.
[4] H. C. Andrews, Digital Image Processing, New York: IEEE, 1978.
[5] Leah J. Siegel, Howard Jay Siegel, And Arthur E. Feather, Parallel Processing Approaches To Image Correlation, IEEE, Transactions On Computers, vol. C-31, No. 3, March 1982.
[6] PaulBourke, CrossCorrelation,Autocorrelation- 2DPatternIdentification,August 1996.
[7] David Jacobs, Correlation and Convolution, Class Notes for CMSC 426, fall 2005.
[8] Soo-Chang Pei, Jian-Jiun Ding, Jahan Chang, Color Pattern Recognition by Quaternion Correlation, Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan, IEEE.

Paper Type : Research Paper
Title : A Segmented Wavelet Inspired Neural Network Approach To Compress Images
Country : India
Authors : Rekha, Vineet Saini
: 10.9790/0661-0263642       logo
Abstract: The great growth in the use of internet and mobile communication devices has revolutionised the way human beings communicate and make information exchange. The necessity of efficient digital information in those devices is essential. With the advancement of technology we need more data transfer. This requires large bandwidth. Data compression is the process of converting an input data stream (the source stream) into another data stream (the output, the bit-stream, or the compressed stream) that has smaller size. Image compression is the application of data compression on digital images. It is the art and science of reducing the amount of data required to represent an image. The purpose for image compression is to reduce the amount of data required for representing sampled digital images and therefore reduce the cost for storage and transmission. Image compression plays a key role in many important applications, including image database, image communications, remote sensing. In this present work we are providing an approach to perform the image compression using improved neural network approach. Here the improvement is being done using wavelet. In this proposed system we achieve the image compression with better visibility. The system is good even for the low resolution images. In this work we work on bit area and maintain the information of the bits. Because of this as we decompress the image first the decode process is performed to get the bit information and then image restoration is applied to get back the clear visual image. We have applied the work on no. of sample images of different types. We also compared the image quality using MSE and PSNR.
[1] Erez Shermer,et al. " Neural Markovian Predictive Compression: An Algorithm for Online Lossless Data Compression", Data Compression Conference 1068-0314/10© 2010 IEEE, 2010.
[2] Stefan Craciun,et al. "Wireless Transmission of Neural Signals Using Entropy and Mutual Information Compression", IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 1534-4320© 2010 IEEE,2010.
[3] LI Huifang,et al. "A New Method of Image Compression Based On Quantum Neural Network", 2010 International Conference of Information Science and Management Engineering 978-0-7695-4132-7/10© 2010 IEEE,2010.
[4] Aditya V. Padaki,et al. "Improving Performance in Neural Network Based Pulse Compression for Binary and Polyphase Codes",12th International Conference on Computer Modelling and Simulation 978-0-7695-4016-0/10© 2010 IEEE,2010
[5] Leong Kwan Li,et al. " Compression of UV Spectrum with Recurrent Neural Network", 978-1-4244-6878-2/10©2010 IEEE,2010
[6] Jin Wang,at al. " ECG Data Compression Research Based on Wavelet Neural Network",International Conference on Computer, Mechatronics, Control and Electronic Engineering (CMCE) 978-1-4244-7956-6/10©2010 IEEE,2010.
[7] Luo Lincong,at al. "Color Image Compression Based on Quaternion Neural Network Principal Component Analysis", 978-1-4244-7874-3/10©2010 IEEE,2010
[8] Qun-ting Yang,et al. " A Novel Robust Watermarking Scheme Based on Neural Network", 978-1-4244-6837-9/10©2010 IEEE,2010
[9] Vilas Gaidhane,et al. "Image Compression using PCA and Improved Technique with MLP Neural Network",International Conference on Advances in Recent Technologies in Communication and Computing 978-0-7695-4201-0/10© 2010 IEEE, 2010.
[10] GUO Hui,et al. "Wavelet packet and neural network basis medical image compression",978-1-4244-7161-4/10©2010 IEEE,2010.
[11] Wen-Nung Lie,at al. "A Perceptually Lossless Image Compression Scheme Based On Jnd Refinement By Neural Network,"Fourth Pacific-Rim Symposium on Image and Video Technology 978-0-7695-4285-0/10© 2010 IEEE,2010.
[12] T. Kohonen.et al. " Self-Organizing Maps", Springer Verlag, London, 3. edition, 2001.
[13] T. Martinetz and K. Schulten,et al. " A neural gas learns topologies". In T. Kohonen, K. M¨akisara, O. Simula, and J. Kangas, editors, Artificial Neural Networks, pages 397–402. North-Holland, Amsterdam, 1991.
[14] J. D. Murray, W. Vanryper, and D. Russell,et al. "Encyclopedia of Graphics File Formats", O'Reilly UK, Cambridge, 1996.
[15] W. B. Pennebaker and J. L. Mitchell,et al. " JPEG: Still Image Data Compression Standard", Kluwer International, Dordrecht, 1992.

Paper Type : Research Paper
Title : An Approach to Continuous Queries in Unstructured Overlays (Coquos)
Country : India
Authors : S.R.Srikanth, M.K.Madialagan
: 10.9790/0661-0264348       logo
Abstract: The current peer-to-peer (P2P) content distribution systems are running by their simple on-demand content discovery mechanism. The utility of these systems can be improved by incorporating two capabilities, first a mechanism through which peers can register their long term interests with the network so that they can be continuously notified of new data items, and second one is the peers to advertise their contents. Although researchers have proposed a few unstructured overlay-based publish-subscribe systems that provide the above capabilities, most of these systems require complex indexing and routing schemes, which not only make them highly complex but also gives less flexible on propagate the queries around transient peers. This paper argues that for many P2P applications, implementing full-fledged publish-subscribe systems is an complex task. For these applications, we study the alternate continuous query paradigm, which is a best-effort service providing the above two capabilities. we present a scalable and effective middleware, called CoQUOS stands for Continious queries in Unstructured Overlays for supporting continuous queries in unstructured overlay networks. Besides being independent of the overlay topology, CoQUOS preserves the simplicity and flexibility of the unstructured P2P network.
Index Terms: Peer-to-peer networks, Continuous queries, Publish-subscribe systems, Random walk.
[1] Gnutella P2P Network. www.gnutella.com.
[2] Kazaa P2 P Network. www.kazaa.com.
[3] TIB/Rendezvous. White paper, 1999.
[4] S. Androutsellis Theotokis and D. Spinellis. A Survey of Peer to-Peer Content Distribution Technologies. ACM Comput. Surv.2004.
[5] B. Arai, G. Das, D. Gunopulos, and V. Kalogeraki. Approximating Aggregation Queries in Peer-to-Peer Networks. In Proceedings of the 22nd International Conference on Data Engineering (ICDE), 2006.
[6] R. Baldoni, C. Marchetti, A. Virgillito, and R. Vitenberg. Contentbased Publish-Subscribe over Structured Overlay Networks. In Proceedings of ICDCS, 2005.