Marten, Dennis and Heuer, Andreas (2017) Machine Learning on Large Databases: Transforming Hidden Markov Models to SQL Statements. Open Journal of Databases (OJDB), 4 (1). pp. 22-42.
|
Text
OJDB_2017v4i1n02_Marten.pdf Download (9MB) | Preview |
Abstract
Machine Learning is a research field with substantial relevance for many applications in different areas. Because of technical improvements in sensor technology, its value for real life applications has even increased within the last years. Nowadays, it is possible to gather massive amounts of data at any time with comparatively little costs. While this availability of data could be used to develop complex models, its implementation is often narrowed because of limitations in computing power. In order to overcome performance problems, developers have several options, such as improving their hardware, optimizing their code, or use parallelization techniques like the MapReduce framework. Anyhow, these options might be too cost intensive, not suitable, or even too time expensive to learn and realize. Following the premise that developers usually are not SQL experts we would like to discuss another approach in this paper: using transparent database support for Big Data Analytics. Our aim is to automatically transform Machine Learning algorithms to parallel SQL database systems. In this paper, we especially show how a Hidden Markov Model, given in the analytics language R, can be transformed to a sequence of SQL statements. These SQL statements will be the basis for a (inter-operator and intra-operator) parallel execution on parallel DBMS as a second step of our research, not being part of this paper.
| Item Type: | Article |
|---|---|
| Subjects: | Forschungsthemen > Big Data Analytics Autorenart > DBIS-Publikationen Projekte > MuSAMA Rahmenprojekte > PArADISE |
| Depositing User: | Marten |
| Date Deposited: | 23 Aug 2017 07:56 |
| Last Modified: | 23 Aug 2017 07:56 |
| URI: | https://eprints.dbis.informatik.uni-rostock.de/id/eprint/879 |
Actions (login required)
 |
View Item |
