Rialto: A Knowledge Discovery suite for data analysis

被引：5

作者：

Manco, Giuseppe ^{[1
]}

Rullo, Pasquale ^{[2
]}

Gallucci, Lorenzo ^{[3
]}

Paturzo, Mirko ^{[3
]}

机构：

[1] CNR, ICAR, Via Bucci 41c, I-87036 Arcavacata Di Rende, CS, Italy

[2] Univ Calabria, Dip Matemat & Informat, Via Bucci 30b, I-87036 Arcavacata Di Rende, CS, Italy

[3] Exeura Srl, Via PA Cabrai, I-87036 Arcavacata Di Rende, CS, Italy

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2016年 / 59卷

关键词：

Knowledge Discovery process; Data mining; Business analytics platforms; ALGORITHM; LANGUAGE;

D O I：

10.1016/j.eswa.2016.04.022

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A Knowledge Discovery (KD) process is a complex inter-disciplinary task, where different types of techniques coexist and cooperate for the purpose of extracting useful knowledge from large amounts of data. So, it is desirable having a unifying environment, built on a formal basis, where to design and perform the overall process. In this paper we propose a general framework which formalizes a KD process as an algebraic expression, that is, as a composition of operators representing elementary operations on two worlds: the data and the model worlds. Then, we describe a KD platform, named Rialto, based on such a framework. In particular, we provide the design principles of the underlying architecture, highlight the basic features, and provide a number of experimental results aimed at assessing the effectiveness of the design choices. (C) 2016 Elsevier Ltd. All rights reserved.

引用

页码：145 / 164

页数：20

共 33 条

[1]

Abdulsahib A. K., 2015, J THEORETICAL APPL I, V76

[2]

[Anonymous], 2000, J DATA WAREHOUSING

[3]

[Anonymous], 2005, DATA STREAMS ALGORIT

[4]

[Anonymous], P 19 GMA GI WORKSH C

[5]

[Anonymous], 2003, VLDB, DOI [10.1016/B978-012722442-8/50118-X, DOI 10.1016/B978-012722442-8/50118-X]

[6]

Berthold MR., 2009, ACM SIGKDD Explor. Newsl., V11, P26, DOI [DOI 10.1145/1656274.1656280, DOI 10.1145/1656274]

[7] Latent Dirichlet allocation [J].

Blei, DM ;

Ng, AY ;

Jordan, MI .

JOURNAL OF MACHINE LEARNING RESEARCH, 2003, 3 (4-5) :993-1022

[8] Expressive power of an algebra for data mining [J].

Calders, Toon ;

Lakshmanan, Laks V. S. ;

Ng, Raymond T. ;

Paredaens, Jan .

ACM TRANSACTIONS ON DATABASE SYSTEMS, 2006, 31 (04) :1169-1214

[9] Efficient evaluation of queries with mining predicates [J].

Chaudhuri, S ;

Narasayya, V ;

Sarawagi, S .

18TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING, PROCEEDINGS, 2002, :529-540

[10]

Chen XJ, 2007, LECT NOTES COMPUT SC, V4819, P3

← 1 2 3 4 →