DOCSLIB.ORG

Ricardo Baeza-Yates Berthier Ribeiro-Neto

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Ricardo Baeza-Yates Berthier Ribeiro-Neto Modern Information Retrieval The Concepts and Technology behind Search Ricardo Baeza-Yates Berthier Ribeiro-Neto Second edition Addison-Wesley Harlow, England Reading, Massachusetts Menlo Park, California• New York Don Mills, Ontario Amsterdam• Bonn Sydney Singapore• Tokyo Madrid• San Juan• Milan •Mexico City• Seoul Taipei • • • • References [1] I. Aalbersberg. Incremental relevance feedback. In Proc of the Fifteenth Annual International ACM/SIGIR Conference on Research and Development in Information Retrieval, pages 11–22, Denmark, 1992. [2] H. Abdi. Kendall rank correlation. In N. Salkind, editor, Encyclopedia of Measure- ment and Statistics, Thousand Oaks, CA, 2007. Sage. [3] H. Abdi. The Kendall rank correlation coefficient. Technical report, Univ. of Texas at Dallas, 2007. [4] K. Aberer, F. Klemm, M. Rajman, and J. Wu. An architecture for peer-to-peer information retrieval. In Workshop on Peer-to-Peer Information Retrieval,Sheffield, UK, July 2004. [5] S. Abiteboul. Querying semi-structured data. In F. N. Afrati and P. Kolaitis, editors, Int. Conf. on Database Theory (ICDT), number 1186 in LNCS, pages 1–18, Delphi, Greece, 1997. Springer-Verlag. [6] S. Abiteboul, M. Preda, and G. Cobena. Adaptive on-line page importance compu- tation. In Proceedings of the twelfth international conference on World Wide Web, pages 280–290, Budapest, Hungary, 2003. ACM Press. [7] M. Abolhassani and N. Fuhr. Applying the divergence from randomness approach for content-only search in xml documents. Lecture Notes in Computer Science, 2997:409– 420, 2004. [8] M. Abrams, editor. World Wide Web: Beyond the Basics. Prentice Hall, 1998. [9] M. Abrol, N. Latarche, U. Mahadevan, J. Mao, R. Mukherjee, P. Raghavan, M. Tourn, J. Wang, and G. Zhang. Navigating large-scale semi-structured data in business portals. In Proceedings of the 27th VLDB Conference, pages 663–666, Roma, Italy, 2001. http://www.vldb.org/conf/2001/P663.pdf. [10] A. Adams and A. Blandford. Digital libraries’ support for the user’s ’information jour- ney’. In Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital Libraries, pages 160–169, Denver, Colorado, 2005. [11] E. Adar. User 4xxxxx9: Anonymizing query logs. In Query Log Analysis: Social and Technological Challenges, Workshop in WWW’07, 2007. [12] J. Adiego, G. Navarro, and P. de la Fuente. Scm: Structural contexts model for improving compression in semistructured text databases. In Proc. 10th International Symposium on String Processing and Information Retrieval (SPIRE 2003),LNCS 767 768 REFERENCES 2857, pages 153–167. Springer, 2003. Extended version appeared in Information Processing and Management 43(3), May 2007, pp. 769–790. [13] J. Adiego, G. Navarro, and P. de la Fuente. Lempel-Ziv compression of structured text. In Proc. 14th IEEE Data Compression Conference (DCC’04), pages 112–121, 2004. Extended version appeared in JASIST 58(4), 2007, pp. 461–478. [14] M. Adler and M. Mitzenmacher. Towards compressing Web graphs. In Data Com- pression Conference, pages 203–212, 2001. [15] G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender sys- tems: A survey of the state-of-the-art and possible extensions. IEEE Trans. on Knowl. and Data Eng., 17(6):734–749, 2005. [16] D. Agarwal and S. Merugu. Predictive discrete latent-factor models for large-scale dyadic data. In KDD ’07: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 26–35, New York, NY, USA, 2007. ACM. [17] E. Agichtein, E. Brill, and S. Dumais. Improving Web search ranking by incorporating user behavior information. In SIGIR ’06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pages 19–26, New York, NY, USA, 2006. ACM Press. [18] E. Agichtein, E. Brill, S. Dumais, and R. Ragno. Learning user interaction models for predicting Web search result preferences. In Proceedings of the 29th annual interna- tional ACM SIGIR conference on Research and development in information retrieval, pages 3–10. ACM Press, 2006. [19] A. Agogino and J. Ghosh. Increasing pagerank through reinforcement learning. In Proceedings of Intelligent Engineering Systems Through Artificial Neural Networks, volume 12, pages 27–32, St. Louis, Missouri, USA, November 2002. ASME Press. [20] M. Agosti and A. F. Smeaton, editors. Information retrieval and hypertext.Kluwer Academic Publishers, Boston/London/Dordrecht, 1996. [21] R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In J. Bocca, M. Jarke, and C. Zaniolo, editors, 20th Int Conference on Very Large Databases, pages 487–499. Morgan Kaufmann Publishers, 1994. [22] A. Aho and M. Corasick. Efficient string matching: an aid to bibliographic search. Communications of the ACM, 18(6):333–340, June 1975. [23] AIR Workshops: Adversarial Web Retrieval. http://airweb.cse.lehigh.edu/, 2005. [24] A. Al-Maskari, M. Sanderson, and P. Clough. The relationship betweenIReffective- ness measures and user satisfaction. In SIGIR ’07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pages 773–774, 2007. [25] S. M. Alessi and S. R. Trollip. Multimedia for learning: methods and development. Allyn and Bacon, 2001. 580 pages. [26] S. Ali, M. Consens, G. Kazai, and M. Lalmas. A common basis for the evaluation of structured document retrieval. In ACM CIKM International Conference on Infor- mation and Knowledge Management, Bremen, Germany, 2008. In Press. [27] W. Alink, R. Bhoedjang, P. Boncz, and A. de Vries. XIRAF - XML-based indexing and querying for digital forensics. Digital Investigation, 3(Supplement-1):50–58, 2006. [28] Alis Technologies. Web languages hit parade, 1997. REFERENCES 769 [29] J. Allan. Incremental relevance feedback for information filtering. In Proc of the 19th Annual International ACM/SIGIR Conference on Research and Development in Information Retrieval, pages 270–278, Zurich, Switzerland, 1996. [30] J. Allan. Perspectives on information retrieval and speech. In Information Retrieval Techniques for Speech Applications, from the workshop “Information Retrieval Tech- niques for Speech Applications,” held as part of the 24th Annual International ACM SIGIR Conference, pages 1–10, London, UK, 2002. Springer-Verlag. [31] J. Allan. HARD Track overview in TREC 2004 high accuracy retrieval from docu- ments. Proceedings of the Thirteenth Text REtrieval Conference (TREC’04), 2005. [32] B. L. Allen. Information Tasks: Toward a User-Centered Approach to Information Systems. Academic Press, San Diego, CA, 1996. [33] E. L. Allwein, R. E. Schapire, and Y. Singer. Reducing multiclass to binary: A unify- ing approach for margin classifiers. Journal of Machine Learning Research, 1:113–141, 2000. [34] O. Alonso and R. Baeza-Yates. A model for visualizing large answers in WWW. In XVIII Int. Conf. of the Chilean CS Society, pages 2–7, Antofagasta, Chile, 1998. IEEE CS Press. [35] O. Alonso, R. Baeza-Yates, and M. Gertz. Effectiveness of temporal snippets. In WSSP Workshop at the World Wide Web Conference—WWW’09, 2009. [36] O. Alonso and S. Mizzaro. Can we get rid of TREC assessors? Using Mechanical Turk for relevance assessment. In SIGIR Evaluation Workshop, 2009. [37] O. Alonso, D. E. Rose, and B. Stewart. Crowdsourcing for relevance evaluation. SIGIR Forum, 42(2):9–15, 2008. [38] G. Amati. Probability Models for Information Retrieval based on Divergence from Randomness. PhD thesis, Department of Computing Science University of Glasgow, 2003. http://www.dcs.gla.ac.uk/∼gianni/selectedPapers.html. [39] G. Amati and C. van Rijsbergen. Probabilistic models of information retrieval based on measuring divergence from randomness. ACM Transaction on Office and Infor- mation Systems (TOIS), 20(4), 2002. [40] Amazon. Mechanical Turk, 2009. http://www.mturk.com. [41] G. M. Amdahl. Validity of the single-processor approach to achieving large scale com- puting capabilities. In Proc. AFIPS 1967 Spring Joint Computer Conf., volume 30, pages 483–485, Atlantic City, N.J., Apr. 1967. [42] S. Amer-Yahia, C. Botev, J. D¨orre, and J. Shanmugasundaram. Full-Text extensions explained. IBM Systems Journal, 45(2):335–352, 2006. [43] S. Amer-Yahia, C. Botev, and J. Shanmugasundaram. TexQuery: a full-text search extension to XQuery. In 13th international conference on World Wide Web, New York, NY, USA, pages 583–594, 2004. [44] S. Amer-Yahia, P. Case, T. Roelleke, J. Shanmugasundaram, and G. Weikum. Report on the DB/IR panel at SIGMOD 2005. SIGMOD Record, 34(4):71–74, 2005. [45] S. Amer-Yahia, S. Cho, and D. Srivastava. Tree Pattern Relaxation. In Advances in Database Technology - EDBT 2002, 8th International Conference on Extending Database Technology, Prague, Czech Republic, pages 496–513, 2002. [46] S. Amer-Yahia, D. Hiemstra, T. Roelleke, D. Srivastava, and G. Weikum. Ranked XML Querying. In S. Amer-Yahia, D. Srivastava, and G. Weikum, editors, Work- shop on Ranked XML Querying, number 08111 in Dagstuhl Seminar Proceedings, Dagstuhl, Germany, 2008. 770 REFERENCES [47] S. Amer-Yahia and M. Lalmas. XML search: languages, INEX and scoring. SIGMOD Record, 35(4):16–23, 2006. [48] A. Amir, S. Srinivasan, and A. Efrat. Search the audio, browse the video: A generic paradigm for video collections. EURASIP Journal on Applied Signal Processing, 2003(2):209–222, 2003. doi:10.1155/S111086570321012X. [49] E. Amitay and C. Paris. Automatically summarising Web sites - is there a way around it? In Proceedings of the 2000 ACM CIKM International Conference on Information and Knowledge Management, pages 173–179, McLean, VA, USA, November 2000. [50] C. Anderson. The Long Tail: Why the Future of Business Is Selling Less of More. Hyperion, New York, revised edition, 2008. [51] T. Anderson, A. Hussam, B. Plummer, and N. Jacobs. Pie charts for visualizing query term frequency in search results. Proceedings of the Fifth International Conference on Asian Digital Library, pages 440–451, 2002. [52] K. Andrews. Visualising cyberspace: Information visualization in the Harmony In- ternet browser. In Proceedings ’95 Information Visualization, pages 97–104, Atlanta, Oct. 1995. [53] K. Andrews, C. G¨utl, J.
Load more

Recommended publications
  • Pittsburgh Dataworks
    Pittsburgh Dataworks Content Marketing Samples By Matthew J. De Reno e. [email protected] | p. (412) 969-1342 | w. PghDataworks.org As the Pittsburgh Dataworks Content Marketing Strategist, I am responsible for creating, managing, and executing the content marketing strategy which includes the duties of social media, content development, PR, event planning, and website management. Pittsburgh Dataworks is a community outreach organization that was founded by IBM to help promote data science in the greater Pittsburgh area. Figure 1: Created image in Photoshop. Notice the “Techsburgh” text over the river! “#Techsburgh” is my effort to create a #hashtag campaign promoting the Pittsburgh technical community (a work in progress – as of Nov. 2015). Select Social Media Figure 2: Created "Data Wars" theme to promote the 2016 Pittsburgh Data Jam. The Pittsburgh Data Jam is the signature annual event of Pittsburgh Dataworks. The goal of this graphic is to engage high school students by tapping into the buzz surrounding the Star Wars film, The Force Awakens, scheduled for release Dec. 2016. Graphic created in Photoshop. Matt De Reno - 2 | P a g e Figure 3: The “Techsburgh” concept repurposed for a general social media post. Matt De Reno - 3 | P a g e Figure 4: General social media posting to keep participating members of Pittsburgh Dataworks interested in the website and aware of new content on the website. Matt De Reno - 4 | P a g e Figure 5: Example of participating in the big data conversation. Figure 6: This posting resulted in the most impressions and engagements for a Pittsburgh Dataworks Twitter posting (as of Nov.
    [Show full text]
  • Summer 2008 Page 5 Letter from the Outgoing President Greetings Band Alumni!
    The Herald Summer Pitt Band Alumni Council Newsletter 2008 Alumni Band 2008: Pitt vs. Buffalo, September 5-6 Inside this issue: Letter from the Please return the attached registration form. 2 The planned schedule will be similar to prior years: Band Director Alumni Day 3 Friday, September 5, 2008— Practice Field Behind Cost Center Registration Alumni are invited (as well as encouraged & recommended) to attend an Family Ticket 3 optional rehearsal Friday night down the hill from Trees Hall behind the Order Form Cost Center at 6 pm. We will practice the drill and music for our number Letter from the 5 with the Varsity Band during the practice. President Following the practice, we will have pizza and beverages with the senior members of the band. New Officers 5 Saturday, September 6, 2008— Heinz Field 2:00 Tailgate in parking lot (see below) 5:00 Pre-game concert with the Varsity Band outside of Gate A in the amphitheater. March to Victory with the Varsity Band to Heinz Field following the pre- Things to remember: game concert. Take our seats in Heinz Field for 6:00 kickoff Send in your dues! Post-game Tailgate Register for Alumni Day! Send in your family’s ticket order or call the ticket office by August 29 You're Invited to be Part of a New Tradition! If you know someone who did not receive this newsletter, please share In addition to Alumni Band Day this year, you're invited to tailgate with the Pitt it with them! Band at every Pitt home football game.
    [Show full text]
  • COMPLAINT I 1 G
    1 TABLE OF CONTENTS Page 2 I. INTRODUCTION. 1 3 II. NATURE OF THE ACTION. 6 4 III. JURISDICTION AND VENUE.. 10 5 III. THE PARTIES. 11 6 A. The Plaintiff. 11 7 B. The Nominal Defendant. 11 8 C. The Individual Defendants. 11 9 D. The Bank Defendants.. 14 10 E. The Auditor Defendant. 15 11 F. Unnamed Participants. 15 12 IV. STATEMENT OF FACTS. 16 13 A. A Brief History of the Hewlett-Packard Company. 16 14 B. Mark Hurd Rejects Autonomy Acquisition. 17 15 C. HP’s Recent History of Bad Deals and Failures.. 18 16 D. Road to Autonomy: Léo Apotheker Becomes New CEO.. 20 17 E. HP Acquires Autonomy.. 23 18 1. August 18, 2011: HP Announces Autonomy Acquisition.. 23 19 2. September 13, 2011: HP Hypes The Value of the Transformative 20 Autonomy IDOL Technology in Order to Finalize the Autonomy Acquisition. 28 21 3. September 22, 2011: CEO Léo Apotheker Forced Out of HP; New 22 CEO Meg Whitman Continues to Praise the Autonomy IDOL Technology. 30 23 F. HP Ignored Serious Concerns About The Propriety of the Autonomy 24 Acquisition For $11.7 Billion.. 30 25 1. HP’s Chief Financial Officer Warned HP Against the Autonomy Acquisition. 30 26 2. HP Knew About Multiple Reports of Improprieties at Autonomy 27 and Multiple Red Flags About Autonomy. 31 28 3. Analysts Warned of Autonomy’s Outdated Technology.. 34 DERIVATIVE COMPLAINT i 1 G. Multiple Companies Refuse to Acquire Autonomy Because It Was OverPriced.. 36 2 1. Oracle Warns HP of Autonomy’s Overvaluation.
    [Show full text]
  • (R&D) Tax Credit
    Report toThe the Pennsylvania Pennsylvania Department of General Revenue Assembly Bureau of Research on the Research and Development (R&D) Tax Credit The Pennsylvania Department of Revenue Bureau of Research March 15, 2012 Pennsylvania Research and Development Tax Credit Page 1 of 14 The Pennsylvania R&D Tax Credit Statute On May 7, 1997, Act 7 of 1997 created the Pennsylvania research and development (R&D) tax credit. The R&D tax credit provision became Article XVII-B of the Tax Reform Code of 1971 (TRC). The intent of the R&D tax credit was to encourage taxpayers to increase R&D expenditures within the Commonwealth in order to enhance economic growth. The terms and concepts used in the calculation of the Commonwealth’s R&D tax credit are based on the federal government’s R&D tax credit definitions for qualified research expense.1 For R&D tax credits awarded between December 1997 and December 2003, Act 7 of 1997 authorized the Department of Revenue (Department) to approve up to $15 million in total tax credits per fiscal year. Additionally, $3 million of the $15 million was set aside for “small” businesses, where a “small business” is defined as a “for-profit corporation, limited liability company, partnership or proprietorship with net book value of assets totaling…less than five million dollars ($5,000,000).” Over the years, several changes have been made to the R&D tax credit statute. Table 1 lists all of the acts that have changed the R&D tax credit statute, along with the applicable award years, the overall tax credit cap and the “small” business set aside.
    [Show full text]
  • IEEE Pittsburgh Section March 2005 Bulletin
    Pittsburgh Section IEEE Bulletin March 2005/Volume 54, No. 7 www.ewh.ieee.org/r2/pittsburgh From Next Generation Search Engines: the Altavista, Google, What ’s Next? Chair Raul Valdes-Perez, Ph.D. The biggest event for the Altavista was the first mass-market web search engine, but it had problems (user experience, month of February was the technical quality) which Google was able to exploit. Users of Google, MSN, and Yahoo face a celebration of Engineers’ different set of issues which competing technologies are racing to exploit. week at the Carnegie Science Center. We thank Vivisimo, a CMU spinoff based in Squirrel Hill, is the leader in one of these technologies: clustering the IEEE members, of search results into meaningful category folders. Vivisimo hosts its own popular web search engine especially the students from at Clusty.com and recently licensed to AOL Search its clustering technology as part of AOL's the University of Pittsburgh stepped-up competition with Google, MSN, and Yahoo. Vivisimo CEO and co-founder Raul Valdes- at Johnstown campus for Perez, Ph.D. will offer an historical perspective, discuss the inefficiencies of web and enterprise volunteering their time at search, and try his hand at foretelling the future. the 12th IEEE Robot Car Mr. Valdes-Perez has been President and Chairman of Vivísimo Inc. since he co-founded it in June Race. The race was between 2000. Before starting Vivísimo, he was on the Carnegie Mellon computer science department faculty 10 schools from around the since 1991; he is now an Adjunct Associate Professor there.
    [Show full text]
  • Web Query Classification and URL Ranking Based on Click Through Approach
    S.PREETHA et al, International Journal of Computer Science and Mobile Computing Vol.2 Issue. 10, October- 2013, pg. 138-144 Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320–088X IJCSMC, Vol. 2, Issue. 10, October 2013, pg.138 – 144 RESEARCH ARTICLE Web Query Classification and URL Ranking Based On Click through Approach S.PREETHA1, K.N Vimal Shankar2 PG Scholar1, Asst. Professor2 Department of Computer Science & Engineering1, 2 V.S.B. Engineering College, Karur, India1, 2 [email protected], [email protected] Abstract― In a web based application; different users may have different search goals when they submit it to a search engine. For a broad-topic and ambiguous query it is difficult. Here we propose a novel approach to infer user search goals by analyzing search engine query logs. This is typically shown in cases such as these: Different users have different backgrounds and interests. However, effective personalization cannot be achieved without accurate user profiles. We propose a framework that enables large-scale evaluation of personalized search. The goal of personalized IR (information retrieval) is to return search results that better match the user intent. First, we propose a framework to discover different user search goals for a query by clustering the proposed feedback sessions. Feedback sessions are getting constructed from user click-through logs and can efficiently reflect the information needs of users. Second, we propose an approach to generate pseudo-documents to better represent the feedback sessions for clustering.
    [Show full text]
  • Classifying User Search Intents for Query Auto-Completion
    Classifying User Search Intents for Query Auto-Completion Jyun-Yu Jiang Pu-Jen Cheng Department of Computer Science Department of Computer Science University of California, Los Angeles National Taiwan University California 90095, USA Taipei 10617, Taiwan [email protected] [email protected] ABSTRACT completion is to accurately predict the user's intended query The function of query auto-completion1 in modern search with only few keystrokes (or even without any keystroke), engines is to help users formulate queries fast and precisely. thereby helping the user formulate a satisfactory query with Conventional context-aware methods primarily rank candi- less effort and less time. For instance, users can avoid mis- date queries2 according to term- and query- relationships to spelling and ambiguous queries with query completion ser- the context. However, most sessions are extremely short. vices. Hence, how to capture user's search intents for pre- How to capture search intents with such relationships be- cisely predicting the query one is typing is very important. The context information, such as previous queries and comes difficult when the context generally contains only few 3 queries. In this paper, we investigate the feasibility of dis- click-through data in the same session , is usually utilized covering search intents within short context for query auto- to capture user's search intents. It also has been exten- completion. The class distribution of the search session (i.e., sively studied in both query completion [1, 21, 35] and sug- gestion [18, 25, 37]. Previous work primarily focused on cal- issued queries and click behavior) is derived as search in- 2 tents.
    [Show full text]
  • (R&D) Tax Credit
    Report to theThe Pennsylvania Pennsylvania Department General of Revenue Assembly Bureau of Research on the Research and Development (R&D) Tax Credit The Pennsylvania Department of Revenue Bureau of Research March 15, 2010 Pennsylvania Research and Development Tax Credit Page 1 of 15 The Pennsylvania R&D Tax Credit Statute As part of the Fiscal Year 2009-10 budget, Act 48 of 2009 reduced the amount of tax credits available under several tax credit programs that can be awarded during the following two years. Act 48 reduced the total amount of R&D tax credit available in FY 2009-10 from $40 million to $20 million and in FY 2010-11 from $40 million to $18 million. Starting in FY 2011-12, the total amount of R&D tax credit the Department can award returns to $40 million per year. ******** On May 7, 1997, Act 7 of 1997 created the Pennsylvania research and development (R&D) tax credit. The R&D tax credit provision became Article XVII-B of the Tax Reform Code of 1971 (TRC). The intent of the R&D tax credit was to encourage taxpayers to increase R&D expenditures within the Commonwealth in order to enhance economic growth. The terms and concepts used in the calculation of the Commonwealth’s R&D tax credit are based on the federal government’s R&D tax credit definitions for qualified research expense.1 Over the years, several changes have been made to the R&D tax credit statute. On December 23, 2003, Governor Edward G. Rendell signed Act 46 of 2003 into law.
    [Show full text]
  • Brochure-2015.Pdf
    Designed by: Yin Zheng Kenan Zhang Qiren Wang CMU SUMMIT US-CHINA INNOVATION AND ENTREPRENEURSHIP 2015 APRIL 25-26, 2015 Brochure Designer: Yin Zheng CARNEGIE MELLON UNIVERSITY Graphic Designers: Kenan Zhang Qiren Wang Welcome Letter from Chair Table of Content Dear All, On behalf of the organizing team of the 4th CMU Summit About CMU Summit ...................................1 on US-China Innovation and Entrepreneurship, I would like to express our warmest welcome to you. Welcome to our CMU & Pittsburgh.......................................2 conference, to Carnegie Mellon University and to Pittsburgh. Debut on April 28, 2012, CMU Summit has become an iconic and top tier professional event in Eastern and Central US. Agenda ......................................................3 In the past few years, our Summit brought huge impacts to Carnegie Mellon and its local and regional community, and Openning Remarks.....................................5 gained wide media coverage. This year, we would like to carry on the legacy from the previous three conferences and to Keynote Speakers ......................................7 continue our mission of sharing the emerging challenges in China and the US. Today, we are honored to have leading entrepreneurs, Panel Speakers ..........................................9 company executives, venture capitalists and researchers from both China and the US. They join us in Carnegie Mellon New Venture Competition ..........................19 Bing Zhou University to address hot issues in various domains, such
    [Show full text]
  • Software Equity Group's 2013 M&A Survey
    The SftSoftware I Idndust ry Financial Report Software Equity Group, L.L.C. 12220 El Camino Real Suite 320 San Diego, CA 92130 [email protected] (858) 509-2800 Unmatched Expertise. Extraordinary Results Overview Deal Team Software Equity Group is an investment bank and M&A advisory firm serving the software and technology sectors. Founded in 1992, our firm has guided and advised companies on five continents, including Ken Bender privately-held software and technology companies in the United States, Canada, Europe, Asia Pacific, Managing Director Africa and Israel. We have represented public companies listed on the NASDAQ, NYSE, American, (858) 509-2800 ext. 222 Toronto, London and Euronext exchanges. Software Equity Group also advises several of the world's [email protected] leading private equity firms. We are ranked among the top ten investment banks worldwide for application software mergers and acquisitions. R. Allen Cinzori Managing Director Services (858) 509-2800 ext. 226 [email protected] Our value proposition is unique and compelling. We are skilled and accomplished investment bankers with extraordinary software, internet and technology domain expertise. Our industry knowledge and experience span virtually every software product category, technology, market and delivery model. We Dennis Clerke have profound understanding of software company finances, operations and valuation. We monitor and Executive Vice President analyze every publicly disclosed software M&A transaction, as well as the market, economy and (858) 509-2800 ext. 233 technology trends that impact these deals. We offer a full complement of M&A execution to our clients [email protected] worldwide. Our capabilities include:. Brad Weekes Sell-Side Advisory Services – leveraging our extensive industry contacts, skilled professionals and Vice President proven methodology, our practice is focused, primarily on guiding our client s wisely toward the (858) 509-2800 ext.
    [Show full text]
  • 2012-Sba-Semi-Annual-Report.Original.Pdf
    Established in 1980 with a mission to grow the economy of Pennsylvania through its entrepreneurs, the 18 university- and college-based centers provided timely consulting, education, and information to new and existing business owners. The SBDC focuses on three core areas: no-fee confidential business consulting, educational programming, and timely, useful information. This report reviews the economic environment and SBA-funded activities of the centers to support small businesses from January-June 2012. The SBDC utilizes a staff of experienced professionals to serve their customers in both traditional business assistance as well as offering specialized consulting. In the first half of calendar year 2012, the SBDC delivered 39,119 hours of no-fee management consulting. Consulting is confidential and provided at no charge by a staff of skilled professionals that emphasize education and guidance in finding practical solutions to business problems. Educational workshops and conferences aid entrepreneurs in articulating goals, evaluating and improving business ideas, researching and attracting needed advisors and resources, and writing business plans to build their businesses. During the first half of the calendar year, 5,182 individuals and firms benefited directly from 257 educational programs. The Pennsylvania SBDC network also responded to numerous inquiries, circulated printed materials, updated websites, and provided critical information to partners to address small business concerns. Impact resulting from these activities is evidenced by the success stories in Section 3 – APPENDICES and described in detail throughout this report. The mission of the network of Pennsylvania Small Business Development Centers is to grow the economy of Pennsylvania by providing entrepreneurs with the education, information and tools necessary to build successful businesses.
    [Show full text]
  • Automatic Web Query Classification Using Labeled and Unlabeled Training Data Steven M
    Automatic Web Query Classification Using Labeled and Unlabeled Training Data Steven M. Beitzel, Eric C. Jensen, David D. Lewis, Abdur Chowdhury, Ophir Frieder, David Grossman Aleksandr Kolcz Information Retrieval Laboratory America Online, Inc. Illinois Institute of Technology [email protected] {steve,ej,ophir,dagr}@ir.iit.edu {cabdur,arkolcz}@aol.com ABSTRACT We examine three methods for classifying general web queries: Accurate topical categorization of user queries allows for exact match against a large set of manually classified queries, a increased effectiveness, efficiency, and revenue potential in weighted automatic classifier trained using supervised learning, general-purpose web search systems. Such categorization and a rule-based automatic classifier produced by mining becomes critical if the system is to return results not just from a selectional preferences from hundreds of millions of unlabeled general web collection but from topic-specific databases as well. queries. The key contribution of this work is the development Maintaining sufficient categorization recall is very difficult as of a query classification framework that leverages the strengths web queries are typically short, yielding few features per query. of all three individual approaches to achieve the most effective We examine three approaches to topical categorization of classification possible. Our combined approach outperforms general web queries: matching against a list of manually labeled each individual method, particularly improves recall, and allows queries, supervised learning of classifiers, and mining of us to effectively classify a much larger proportion of an selectional preference rules from large unlabeled query logs. operational web query stream. Each approach has its advantages in tackling the web query classification recall problem, and combining the three 2.
    [Show full text]