3 research outputs found
Exploiting subspace distance equalities in Highdimensional data for knn queries
Efficient k-nearest neighbor computation for high-dimensional data is an important, yet challenging task. The response times of stateof-the-art indexing approaches highly depend on factors like distribution of the data. For clustered data, such approaches are several factors faster than a sequential scan. However, if various dimensions contain uniform or Gaussian data they tend to be clearly outperformed by a simple sequential scan. Hence, we require for an approach generally delivering good response times, independent of the data distribution. As solution, we propose to exploit a novel concept to efficiently compute nearest neighbors. We name it sub-space distance equality, which aims at reducing the number of distance computations independent of the data distribution. We integrate knn computing algorithms into the Elf index structure allowing to study the sub-space distance equality concept in isolation and in combination with a main-memory optimized storage layout. In a large comparative study with twelve data sets, our results indicate that indexes based on sub-space distance equalities compute the least amount of distances. For clustered data, our Elf knn algorithm delivers at least a performance increase of factor two up to an increase of two magnitudes without losing the performance gain compared to sequential scans for uniform or Gaussian data
Method for the semantic indexing of concept hierarchies, uniform representation, use of relational database systems and generic and case-based reasoning
This paper presents a method for semantic indexing and describes its
application in the field of knowledge representation. Starting point of the
semantic indexing is the knowledge represented by concept hierarchies. The goal
is to assign keys to nodes (concepts) that are hierarchically ordered and
syntactically and semantically correct. With the indexing algorithm, keys are
computed such that concepts are partially unifiable with all more specific
concepts and only semantically correct concepts are allowed to be added. The
keys represent terminological relationships. Correctness and completeness of
the underlying indexing algorithm are proven. The use of classical relational
databases for the storage of instances is described. Because of the uniform
representation, inference can be done using case-based reasoning and generic
problem solving methods