TY - GEN

T1 - An indexing structure for dynamic multidimensional data in vector space.

AU - Mikhaylova, E.

AU - Novikov, B.

AU - Volokhov, A.

PY - 2013

Y1 - 2013

N2 - The multidimensional k – NN (k nearest neighbors) query problem is relevant to a large variety of database applications, including information retrieval, natural language processing, and data mining. To solve it efficiently, the database needs an indexing structure that provides this kind of search. However, attempts to find an exact solution are hardly feasible in multidimensional space. In this paper, a novel indexing technique for the approximate solution of k – NN problem is described and analyzed. The construction of the indexing tree is based on clustering. Indexing structure is implemented on top of high-performance industrial DBMS.

AB - The multidimensional k – NN (k nearest neighbors) query problem is relevant to a large variety of database applications, including information retrieval, natural language processing, and data mining. To solve it efficiently, the database needs an indexing structure that provides this kind of search. However, attempts to find an exact solution are hardly feasible in multidimensional space. In this paper, a novel indexing technique for the approximate solution of k – NN problem is described and analyzed. The construction of the indexing tree is based on clustering. Indexing structure is implemented on top of high-performance industrial DBMS.

KW - multidimensional indexing structure

KW - k-NN

U2 - 10.1007/978-3-642-32741-4_17

DO - 10.1007/978-3-642-32741-4_17

M3 - Conference contribution

SN - 9783642327407

SP - 185

EP - 193

BT - An indexing structure for dynamic multidimensional data in vector space.

ER -