Multimedia Data Querying
By its very nature, multimedia data querying shares the three V challenges ([V]olume, [V]elocity, and [V]ariety) of the so called “Big Data” applications. Systems supporting multimedia data querying, however, must tackle additional, more specific, challenges, including those posed by the [H]igh-dimensional, [M]ulti-modal (temporal, spatial, hierarchical, and graph-structured), and inter-[L]inked nature of most multimedia data as well as the [I]mprecision of the media features and [S]parsity of the observations in the real-world.
Moreover, since the end-users for most multimedia data querying tasks are us (i.e., humans), we need to consider fundamental constraints posed by [H]umanbeings, from the difficulties they face in providing unambiguous specifications of interest or preference, subjectivity in their interpretations of results, and their limitations in perception and memory. Last, but not the least, since a large portion of multimedia data is human-centered, we also...
- 3.Andoni A, Indyk, P. Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. In: Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science; 2006. p. 459–68.Google Scholar
- 6.Candan KS, Sapino ML. Data management for multimedia retrieval. Cambridge University Press; 2010. ISBN-10:0521887399, ISBN-13: 978-0521887397.Google Scholar
- 11.Fagin R. Fuzzy queries in multimedia database systems. In: Proceedings of the 17th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems; 1998. p. 1–10.Google Scholar
- 13.Harshman RA. Foundations of the parafac procedure: models and conditions for an “explanatory” multi-modal factor analysis. UCLA Work Papers Phon. 1970;16:1–84.Google Scholar
- 14.Hofmann T. Probabilistic latent semantic indexing. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval; 1999. p. 50–7.Google Scholar
- 17.Li C, Chang KC-C, Ilyas IF, Song S. RankSQL: Query algebra and optimization for relational top-k queries. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2005. p. 131–42.Google Scholar
- 18.Pearl J. Bayesian networks: a model of self-activated memory for evidential reasoning. In: Proceedings of the 7th Conference of the Cognitive Science Society; 1985. p. 329–34.Google Scholar
- 19.Qi Y, Candan KS, Sapino ML. Sum-Max monotonic ranked joins for evaluating top-K twig queries on weighted data graphs. In: Proceedings of the 33rd International Conference on Very Large Data Bases; 2007. p. 507–18.Google Scholar
- 22.Zhang Z, Hwang S, Chang KC, Wang M, Lang CA, Chang Y. Boolean + Ranking: querying a database by K-constrained optimization. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; n.d. p. 359–70.Google Scholar