Abstract
LDAP directories have rapidly emerged as the essential framework for storing a wide range of heterogeneous information under various applications and services. Increasing amounts of information are being stored in LDAP directories imposing the need for efficient data organization and retrieval. In this paper, we propose the LPAIR & LMERGE (LP-LM) hierarchical agglomerative clustering algorithm for improving LDAP data organization. LP-LM merges a pair of clusters at each step, considering the LD-vectors, which represent the entries’ structure. The clustering-based LDAP data organization enhances LDAP server’s response times, under a specific query framework.
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References
Chadwick, D.: Deficiencies in LDAP when used to support PKI. Communications of the ACM 46, 99–104 (2003)
Fan, Q., Wu, Q., He, Y., Huang, J.: Optimized Strategies of Grid Information Services. In: Proc. of the First Int. Conf. on Semantics, Knowledge, and Grid, p. 90 (2005)
Gemmill, J., Chatterjee, S., Miller, T., Verharen, E.: ViDe.Net Middleware for Scalable Video Services for Research and Higher Education. In: ACM Southeastern Conf. GA. ACM 1-58113-675-7/030/03, pp. 463–468 (2003)
Guha, S., Rastogi, R., Shim, K.: ROCK: A Robust Clustering Algorithm For Categorical Attributes. In: Proc. 15th Int. Conf. Data Eng., pp. 512–521 (1999)
Howes, T., Smith, M.: LDAP: Programming Directory-Enabled Applications with Lightweight Directory Access Protocol. Macmillan Technical Publishing, Basingstoke (1997)
Koutsonikola, V., Vakali, A.: LDAP: Framework, Practices, and Trends. IEEE Internet Computing 8, 66–72 (2004)
Kumar, A., Gupta, R.: Edge Caching for Directory Based Web Applications: Algorithms and Performance. In: Proc. of the 8th international workshop in Web content caching and distribution, pp. 39–56 (2004)
Lee, H., Mun, S.-G., Huh, E.-N., Choo, H.: Efficient Data Indexing System Based on OpenLDAP in Data Grid. In: Int. Conf. on Computational Science, vol. 1, pp. 960–964 (2006)
Li, T.: A Unified View on Clustering Binary Data. Machine Learning 62, 199–215 (2006)
Lian, W., Cheung, D., Mamoulis, N., Yiu, S.-M.: An Efficient and Scalable Algorithm for Clustering XML Documents by Structure. IEEE Trans. on Knowledge and Data Engineering 16, 82–96 (2004)
Liang, J., Vaishnavi, V., Vandenberg, A.: Clustering of LDAP directory schemas to facilitate information resources interoperability across organizations. IEEE Trans. on Systems, Man and Cybernetics, Part A 36, 631–642 (2006)
Lim, S., Choi, J., Zeilenga, K.: Design and Implementation of LDAP Component Matching for Flexible and Secure Certificate Access in PKI. In: Proc. of the 4th Annual PKI R&D Workshop, pp. 41–51 (2005)
Park, J., Sandhu, R., Ahn, G.-J.: Role-based access control on the web. ACM Trans. on Information and System Security (TISSEC) 4, 37–71 (2001)
Ponaramenko, J., Bourne, P., Shindyalov, I.: Building an Automated Classification of DNA-binding Protein Domains. Bioinformatics 18, S192–S201 (2002)
Whal, M., Howes, T., Kille, S.: Lightweight Directory Access Protocol (v3). IETF RFC 2251 (1997)
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Koutsonikola, V., Vakali, A., Mpalasas, A., Valavanis, M. (2008). A Structure-Based Clustering on LDAP Directory Information. In: An, A., Matwin, S., Raś, Z.W., Ślęzak, D. (eds) Foundations of Intelligent Systems. ISMIS 2008. Lecture Notes in Computer Science(), vol 4994. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68123-6_13
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DOI: https://doi.org/10.1007/978-3-540-68123-6_13
Publisher Name: Springer, Berlin, Heidelberg
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