@article {16897,
	title = {Properties of embedding methods for similarity searching in metric spaces},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {25},
	year = {2003},
	month = {2003/05//},
	pages = {530 - 549},
	abstract = {Complex data types-such as images, documents, DNA sequences, etc.-are becoming increasingly important in modern database applications. A typical query in many of these applications seeks to find objects that are similar to some target object, where (dis)similarity is defined by some distance function. Often, the cost of evaluating the distance between two objects is very high. Thus, the number of distance evaluations should be kept at a minimum, while (ideally) maintaining the quality of the result. One way to approach this goal is to embed the data objects in a vector space so that the distances of the embedded objects approximates the actual distances. Thus, queries can be performed (for the most part) on the embedded objects. We are especially interested in examining the issue of whether or not the embedding methods will ensure that no relevant objects are left out. Particular attention is paid to the SparseMap, FastMap, and MetricMap embedding methods. SparseMap is a variant of Lipschitz embeddings, while FastMap and MetricMap are inspired by dimension reduction methods for Euclidean spaces. We show that, in general, none of these embedding methods guarantee that queries on the embedded objects have no false dismissals, while also demonstrating the limited cases in which the guarantee does hold. Moreover, we describe a variant of SparseMap that allows queries with no false dismissals. In addition, we show that with FastMap and MetricMap, the distances of the embedded objects can be much greater than the actual distances. This makes it impossible (or at least impractical) to modify FastMap and MetricMap to guarantee no false dismissals.},
	keywords = {complex, contractiveness;, data, databases;, decomposition;, dimension, distance, distortion;, DNA, documents;, EMBEDDING, embeddings;, Euclidean, evaluations;, FastMap;, images;, Lipschitz, methods;, metric, MetricMap;, multimedia, processing;, query, reduction, search;, searching;, sequences;, similarity, singular, spaces;, SparseMap;, types;, value},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2003.1195989},
	author = {Hjaltason,G. R and Samet, Hanan}
}
@conference {12107,
	title = {A light-weight process for capturing and evolving defect reduction experience},
	booktitle = {Engineering of Complex Computer Systems, 2002. Proceedings. Eighth IEEE International Conference on},
	year = {2002},
	month = {2002/12//},
	pages = {129 - 132},
	abstract = {Selecting technologies for developing software is a crucial activity in software projects. Defect reduction is an example of an area in which software developers have to decide what technologies to use. CeBASE is a NSF funded project that has the role of improving software development by providing decision support on the selection of techniques and tools. The decision support is based on empirical data organized in experience bases and refined into high-level models. Empirical data is collected through various activities, for example through eWorkshops in which experts discuss important issues, and formalized using the lightweight knowledge dust to knowledge pearl process.},
	keywords = {bases;experience, CeBASE;NSF, defect, development;software, experience;software, funded, knowledge;software, management;lightweight, management;software, project;decision, projects;software, reduction, reusability;software, reuse;software, support;eWorkshops;experience, tools;, tools;knowledge},
	doi = {10.1109/ICECCS.2002.1181505},
	author = {Basili, Victor R. and Lindvall,M. and Shull, F.}
}
