{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T17:50:40Z","timestamp":1725558640931},"publisher-location":"Berlin, Heidelberg","reference-count":23,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"type":"print","value":"9783642143991"},{"type":"electronic","value":"9783642144004"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2010]]},"DOI":"10.1007\/978-3-642-14400-4_22","type":"book-chapter","created":{"date-parts":[[2010,6,26]],"date-time":"2010-06-26T05:18:09Z","timestamp":1277529489000},"page":"277-291","source":"Crossref","is-referenced-by-count":1,"title":["Mining for Paths in Flow Graphs"],"prefix":"10.1007","author":[{"given":"Adam","family":"Jocksch","sequence":"first","affiliation":[]},{"given":"Jos\u00e9 Nelson","family":"Amaral","sequence":"additional","affiliation":[]},{"given":"Marcel","family":"Mitran","sequence":"additional","affiliation":[]}],"member":"297","reference":[{"key":"22_CR1","doi-asserted-by":"crossref","unstructured":"Agrawal, R., Imieli\u0144ski, T., Swami, A.: Mining association rules between sets of items in large databases. In: SIGMOD International Conference on Management of Data, Washington, DC, USA, pp. 207\u2013216 (1993)","DOI":"10.1145\/170036.170072"},{"key":"22_CR2","unstructured":"Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: International Conference on Very Large Data Bases (VLDB), Santiago, Chile, September 1994, pp. 487\u2013499 (1994)"},{"key":"22_CR3","doi-asserted-by":"crossref","unstructured":"Agrawal, R., Srikant, R.: Mining sequential patterns. In: International Conference on Data Engineering (ICDE), Taipei, Taiwan, March 1995, pp. 3\u201314 (1995)","DOI":"10.1109\/ICDE.1995.380415"},{"key":"22_CR4","doi-asserted-by":"crossref","unstructured":"Ball, T., Mataga, P., Sagiv, M.: Edge profiling versus path profiling: the showdown. In: Principles of Programming Languages (POPL), San Diego, California, United States, pp. 134\u2013148 (1998)","DOI":"10.1145\/268946.268958"},{"key":"22_CR5","doi-asserted-by":"crossref","unstructured":"Geng, R., Dong, X., Zhang, X., Xu, W.: Efficiently mining closed frequent patterns with weight constraint from directed graph traversals using weighted FP-tree approach. In: Intern. Coll. on Computing, Communication, Control, and Management, Guangzhou City, China, August 2008, pp. 399\u2013403 (2008)","DOI":"10.1109\/CCCM.2008.393"},{"key":"22_CR6","unstructured":"Grcevski, N., Kielstra, A., Stoodley, K., Stoodley, M., Sundaresan, V.: Java just-in-time compiler and virtual machine improvements for server and middleware applications. In: Conf. on Virtual Machine Research and Technology Symposium (VM), San Jose, CA, USA, p. 12. USENIX Assoc. (2004)"},{"key":"22_CR7","doi-asserted-by":"crossref","unstructured":"Hasan, M.A., Chaoji, V., Salem, S., Besson, J., Zaki, M.: Origami: Mining representative orthogonal graph patterns. In: International Conference on Data Mining (ICDM), Washington, DC, USA, pp. 153\u2013162 (2007)","DOI":"10.1109\/ICDM.2007.45"},{"key":"22_CR8","unstructured":"Hwang, C.-C., Huang, S.-K., Chen, D.-J., Chen, D.T.K.: Object-oriented program behavior analysis based on control patterns. In: Asia-Pacific Conf. on Quality Software, Hong Kong, China, December 2001, pp. 81\u201387 (2001)"},{"key":"22_CR9","unstructured":"IBM Corporation. WebSphere Application Server (March 2009), http:\/\/www-01.ibm.com\/software\/websphere\/"},{"key":"22_CR10","series-title":"Lecture Notes in Artificial Intelligence","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/3-540-45372-5_2","volume-title":"Principles of Data Mining and Knowledge Discovery","author":"A. Inokuchi","year":"2000","unstructured":"Inokuchi, A., Washio, T., Motoda, H.: An apriori-based algorithm for mining frequent substructures from graph data. In: Zighed, D.A., Komorowski, J., \u017bytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol.\u00a01910, pp. 13\u201323. Springer, Heidelberg (2000)"},{"key":"22_CR11","series-title":"Lecture Notes in Artificial Intelligence","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1007\/3-540-48912-6_56","volume-title":"Methodologies for Knowledge Discovery and Data Mining","author":"A. Inokuchi","year":"1999","unstructured":"Inokuchi, A., Washio, T., Motoda, H., Kumasawa, K., Arai, N.: Basket analysis for graph structured data. In: Zhong, N., Zhou, L. (eds.) PAKDD 1999. LNCS (LNAI), vol.\u00a01574, pp. 420\u2013431. Springer, Heidelberg (1999)"},{"key":"22_CR12","doi-asserted-by":"crossref","unstructured":"Jocksch, A., Mitran, M., Siu, J., Grcevski, N., Amaral, J.N.: Mining opportuinities for code improvement in a just-in-time compiler. In: Compiler Construction (CC), Paphos, Cyprus (March 2010)","DOI":"10.1007\/978-3-642-11970-5_2"},{"issue":"5A","key":"22_CR13","first-page":"136","volume":"6","author":"S.D. Lee","year":"2006","unstructured":"Lee, S.D., Park, H.C.: Mining frequent patterns from weighted traversals on graph using confidence interval and pattern priority. Intern. Journal of Computer Science and Network Security\u00a06(5A), 136\u2013141 (2006)","journal-title":"Intern. Journal of Computer Science and Network Security"},{"key":"22_CR14","unstructured":"Mannila, H., Toivonen, H., Verkamo, A.I.: Discovering Frequent Episodes in Sequences. In: Fayyad, U.M., Uthurusamy, R. (eds.) Knowledge Discovery and Data Mining (KDD), Montreal, Canada (1995)"},{"key":"22_CR15","doi-asserted-by":"crossref","unstructured":"Moseley, T., Grunwald, D., Peri, R.V.: Optiscope: Performance accountability for optimizing compilers. In: Code Generation and Optimization (CGO), Seattle, WA, USA (2009)","DOI":"10.1109\/CGO.2009.26"},{"key":"22_CR16","doi-asserted-by":"crossref","unstructured":"Nagpurkar, P., Cain, H.W., Serrano, M., Choi, J.-D., Krintz, R.: A study of instruction cache performance and the potential for instruction prefetching in J2EE server applications. In: Workshop of Computer Architecture Evaluation using Commercial Workloads, Phoenix, AZ, USA (2007)","DOI":"10.1109\/PACT.2007.4336207"},{"key":"22_CR17","doi-asserted-by":"crossref","unstructured":"Nijssen, S., Kok, J.N.: A quickstart in frequent structure mining can make a difference. In: Knowledge Discovery and Data Mining (KDD), Seattle, WA, USA, pp. 647\u2013652 (2004)","DOI":"10.1145\/1014052.1014134"},{"key":"22_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/11574798_1","volume-title":"Transactions on Rough Sets III","author":"Z. Pawlak","year":"2005","unstructured":"Pawlak, Z.: Flow graphs and data mining. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets III. LNCS, vol.\u00a03400, pp. 1\u201336. Springer, Heidelberg (2005)"},{"key":"22_CR19","unstructured":"Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., Hsu, M.C.: PrefixSpan mining sequential patterns efficiently by prefix projected pattern growth. In: International Conference on Data Engineering (ICDE), Heidelberg, Germany, pp. 215\u2013226 (2001)"},{"key":"22_CR20","first-page":"3","volume-title":"Advances in Database Techn.","author":"R. Srikant","year":"1996","unstructured":"Srikant, R., Agrawal, R.: Mining Sequential Patterns: Generalizations and Performance Improvements. In: Advances in Database Techn., pp. 3\u201317. Springer, Heidelberg (1996)"},{"issue":"2","key":"22_CR21","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1109\/MM.2008.26","volume":"28","author":"C.F. Webb","year":"2008","unstructured":"Webb, C.F.: IBM z10: The next generation microprocessor. IEEE Micro\u00a028(2), 19\u201329 (2008)","journal-title":"IEEE Micro"},{"key":"22_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1007\/978-3-540-68416-9_12","volume-title":"Discovery of Frequent Graph Patterns that Consist of the Vertices with the Complex Structures","author":"T. Yamamoto","year":"2008","unstructured":"Yamamoto, T., Ozaki, T., Ohkawa, T.: Discovery of Frequent Graph Patterns that Consist of the Vertices with the Complex Structures. LNCS, pp. 143\u2013156. Springer, Heidelberg (2008)"},{"key":"22_CR23","unstructured":"Yan, X., Han, J.: gSpan: Graph-based substructure pattern mining. In: International Conference on Data Mining (ICDM), Washington, DC, USA, p. 721 (2002)"}],"container-title":["Lecture Notes in Computer Science","Advances in Data Mining. Applications and Theoretical Aspects"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-642-14400-4_22.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,24]],"date-time":"2020-11-24T02:52:09Z","timestamp":1606186329000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-642-14400-4_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2010]]},"ISBN":["9783642143991","9783642144004"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-642-14400-4_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2010]]}}}