2010
5.
Wicker, Jörg; Richter, Lothar; Kramer, Stefan
SINDBAD and SiQL: Overview, Applications and Future Developments Book Section
In: Džeroski, Sašo; Goethals, Bart; Panov, Panče (Ed.): Inductive Databases and Constraint-Based Data Mining, pp. 289-309, Springer New York, 2010, ISBN: 978-1-4419-7737-3.
Abstract | Links | BibTeX | Altmetric | PlumX | Tags: data mining, inductive databases, machine learning, query languages
@incollection{wicker2010sindbad,
title = {SINDBAD and SiQL: Overview, Applications and Future Developments},
author = {J\"{o}rg Wicker and Lothar Richter and Stefan Kramer},
editor = {Sa\v{s}o D\v{z}eroski and Bart Goethals and Pan\v{c}e Panov},
url = {http://dx.doi.org/10.1007/978-1-4419-7738-0_12},
doi = {10.1007/978-1-4419-7738-0_12},
isbn = {978-1-4419-7737-3},
year = {2010},
date = {2010-01-01},
booktitle = {Inductive Databases and Constraint-Based Data Mining},
pages = {289-309},
publisher = {Springer New York},
abstract = {The chapter gives an overview of the current state of the Sindbad system and planned extensions. Following an introduction to the system and its query language SiQL, we present application scenarios from the areas of gene expression/regulation and small molecules. Next, we describe a web service interface to Sindbad that enables new possibilities for inductive databases (distributing tasks over multiple servers, language and platform independence, …). Finally, we discuss future plans for the system, in particular, to make the system more ‘declarative’ by the use of signatures, to integrate the useful concept of mining views into the system, and to support specific pattern domains like graphs and strings.},
keywords = {data mining, inductive databases, machine learning, query languages},
pubstate = {published},
tppubtype = {incollection}
}
The chapter gives an overview of the current state of the Sindbad system and planned extensions. Following an introduction to the system and its query language SiQL, we present application scenarios from the areas of gene expression/regulation and small molecules. Next, we describe a web service interface to Sindbad that enables new possibilities for inductive databases (distributing tasks over multiple servers, language and platform independence, …). Finally, we discuss future plans for the system, in particular, to make the system more ‘declarative’ by the use of signatures, to integrate the useful concept of mining views into the system, and to support specific pattern domains like graphs and strings.
2008
4.
Wicker, Jörg; Richter, Lothar; Kessler, Kristina; Kramer, Stefan
SINDBAD and SiQL: An Inductive Database and Query Language in the Relational Model Proceedings Article
In: Daelemans, Walter; Goethals, Bart; Morik, Katharina (Ed.): Machine Learning and Knowledge Discovery in Databases, pp. 690-694, Springer Berlin Heidelberg, 2008, ISBN: 978-3-540-87480-5.
Abstract | Links | BibTeX | Altmetric | PlumX | Tags: data mining, inductive databases, machine learning, query languages
@inproceedings{wicker2008sindbad,
title = {SINDBAD and SiQL: An Inductive Database and Query Language in the Relational Model},
author = {J\"{o}rg Wicker and Lothar Richter and Kristina Kessler and Stefan Kramer},
editor = {Walter Daelemans and Bart Goethals and Katharina Morik},
url = {http://dx.doi.org/10.1007/978-3-540-87481-2_48},
doi = {10.1007/978-3-540-87481-2_48},
isbn = {978-3-540-87480-5},
year = {2008},
date = {2008-01-01},
booktitle = {Machine Learning and Knowledge Discovery in Databases},
volume = {5212},
pages = {690-694},
publisher = {Springer Berlin Heidelberg},
series = {Lecture Notes in Computer Science},
abstract = {In this demonstration, we will present the concepts and an implementation of an inductive database \textendash as proposed by Imielinski and Mannila \textendash in the relational model. The goal is to support all steps of the knowledge discovery process on the basis of queries to a database system. The query language SiQL (structured inductive query language), an SQL extension, offers query primitives for feature selection, discretization, pattern mining, clustering, instance-based learning and rule induction. A prototype system processing such queries was implemented as part of the SINDBAD (structured inductive database development) project. To support the analysis of multi-relational data, we incorporated multi-relational distance measures based on set distances and recursive descent. The inclusion of rule-based classification models made it necessary to extend the data model and software architecture significantly. The prototype is applied to three different data sets: gene expression analysis, gene regulation prediction and structure-activity relationships (SARs) of small molecules.},
keywords = {data mining, inductive databases, machine learning, query languages},
pubstate = {published},
tppubtype = {inproceedings}
}
In this demonstration, we will present the concepts and an implementation of an inductive database – as proposed by Imielinski and Mannila – in the relational model. The goal is to support all steps of the knowledge discovery process on the basis of queries to a database system. The query language SiQL (structured inductive query language), an SQL extension, offers query primitives for feature selection, discretization, pattern mining, clustering, instance-based learning and rule induction. A prototype system processing such queries was implemented as part of the SINDBAD (structured inductive database development) project. To support the analysis of multi-relational data, we incorporated multi-relational distance measures based on set distances and recursive descent. The inclusion of rule-based classification models made it necessary to extend the data model and software architecture significantly. The prototype is applied to three different data sets: gene expression analysis, gene regulation prediction and structure-activity relationships (SARs) of small molecules.
3.
Richter, Lothar; Wicker, Jörg; Kessler, Kristina; Kramer, Stefan
An Inductive Database and Query Language in the Relational Model Proceedings Article
In: Proceedings of the 11th International Conference on Extending Database Technology: Advances in Database Technology, pp. 740–744, ACM, 2008, ISBN: 978-1-59593-926-5.
Abstract | Links | BibTeX | Altmetric | PlumX | Tags: data mining, inductive databases, machine learning, query languages
@inproceedings{richter2008inductive,
title = {An Inductive Database and Query Language in the Relational Model},
author = {Lothar Richter and J\"{o}rg Wicker and Kristina Kessler and Stefan Kramer},
url = {https://wicker.nz/nwp-acm/authorize.php?id=N10033
http://doi.acm.org/10.1145/1353343.1353440},
doi = {10.1145/1353343.1353440},
isbn = {978-1-59593-926-5},
year = {2008},
date = {2008-01-01},
booktitle = {Proceedings of the 11th International Conference on Extending Database Technology: Advances in Database Technology},
pages = {740--744},
publisher = {ACM},
series = {EDBT '08},
abstract = {In the demonstration, we will present the concepts and an implementation of an inductive database -- as proposed by Imielinski and Mannila -- in the relational model. The goal is to support all steps of the knowledge discovery process, from pre-processing via data mining to post-processing, on the basis of queries to a database system. The query language SIQL (structured inductive query language), an SQL extension, offers query primitives for feature selection, discretization, pattern mining, clustering, instance-based learning and rule induction. A prototype system processing such queries was implemented as part of the SINDBAD (structured inductive database development) project. Key concepts of this system, among others, are the closure of operators and distances between objects. To support the analysis of multi-relational data, we incorporated multi-relational distance measures based on set distances and recursive descent. The inclusion of rule-based classification models made it necessary to extend the data model and the software architecture significantly. The prototype is applied to three different applications: gene expression analysis, gene regulation prediction and structure-activity relationships (SARs) of small molecules.},
keywords = {data mining, inductive databases, machine learning, query languages},
pubstate = {published},
tppubtype = {inproceedings}
}
In the demonstration, we will present the concepts and an implementation of an inductive database — as proposed by Imielinski and Mannila — in the relational model. The goal is to support all steps of the knowledge discovery process, from pre-processing via data mining to post-processing, on the basis of queries to a database system. The query language SIQL (structured inductive query language), an SQL extension, offers query primitives for feature selection, discretization, pattern mining, clustering, instance-based learning and rule induction. A prototype system processing such queries was implemented as part of the SINDBAD (structured inductive database development) project. Key concepts of this system, among others, are the closure of operators and distances between objects. To support the analysis of multi-relational data, we incorporated multi-relational distance measures based on set distances and recursive descent. The inclusion of rule-based classification models made it necessary to extend the data model and the software architecture significantly. The prototype is applied to three different applications: gene expression analysis, gene regulation prediction and structure-activity relationships (SARs) of small molecules.
2.
Wicker, Jörg; Brosdau, Christoph; Richter, Lothar; Kramer, Stefan
SINDBAD SAILS: A Service Architecture for Inductive Learning Schemes Proceedings Article
In: Proceedings of the First Workshop on Third Generation Data Mining: Towards Service-Oriented Knowledge Discovery, 2008.
Abstract | Links | BibTeX | Tags: data mining, inductive databases, machine learning, query languages
@inproceedings{wicker2008sindbadsails,
title = {SINDBAD SAILS: A Service Architecture for Inductive Learning Schemes},
author = {J\"{o}rg Wicker and Christoph Brosdau and Lothar Richter and Stefan Kramer},
url = {http://www.ecmlpkdd2008.org/files/pdf/workshops/sokd/2.pdf},
year = {2008},
date = {2008-01-01},
booktitle = {Proceedings of the First Workshop on Third Generation Data Mining: Towards Service-Oriented Knowledge Discovery},
abstract = {The paper presents SINDBAD SAILS (Service Architecture for Inductive Learning Schemes), a Web Service interface to the inductive database SINDBAD. To the best of our knowledge, it is the first time a Web Service interface is provided for an inductive database. The combination of service-oriented architectures and inductive databases is particularly useful, as it enables distributed data mining without the need to install specialized data mining or machine learning software. Moreover, inductive queries can easily be used in almost any kind of programming language. The paper discusses the underlying concepts and explains a sample program making use of SINDBAD SAILS.},
keywords = {data mining, inductive databases, machine learning, query languages},
pubstate = {published},
tppubtype = {inproceedings}
}
The paper presents SINDBAD SAILS (Service Architecture for Inductive Learning Schemes), a Web Service interface to the inductive database SINDBAD. To the best of our knowledge, it is the first time a Web Service interface is provided for an inductive database. The combination of service-oriented architectures and inductive databases is particularly useful, as it enables distributed data mining without the need to install specialized data mining or machine learning software. Moreover, inductive queries can easily be used in almost any kind of programming language. The paper discusses the underlying concepts and explains a sample program making use of SINDBAD SAILS.
2006
1.
Kramer, Stefan; Aufschild, Volker; Hapfelmeier, Andreas; Jarasch, Alexander; Kessler, Kristina; Reckow, Stefan; Wicker, Jörg; Richter, Lothar
Inductive Databases in the Relational Model: The Data as the Bridge Proceedings Article
In: Bonchi, Francesco; Boulicaut, Jean-François (Ed.): Knowledge Discovery in Inductive Databases, pp. 124-138, Springer Berlin Heidelberg, 2006, ISBN: 978-3-540-33292-3.
Abstract | Links | BibTeX | Altmetric | PlumX | Tags: data mining, inductive databases, machine learning, query languages
@inproceedings{kramer2006inductive,
title = {Inductive Databases in the Relational Model: The Data as the Bridge},
author = {Stefan Kramer and Volker Aufschild and Andreas Hapfelmeier and Alexander Jarasch and Kristina Kessler and Stefan Reckow and J\"{o}rg Wicker and Lothar Richter},
editor = {Francesco Bonchi and Jean-Fran\c{c}ois Boulicaut},
url = {http://dx.doi.org/10.1007/11733492_8},
doi = {10.1007/11733492_8},
isbn = {978-3-540-33292-3},
year = {2006},
date = {2006-01-01},
booktitle = {Knowledge Discovery in Inductive Databases},
volume = {3933},
pages = {124-138},
publisher = {Springer Berlin Heidelberg},
series = {Lecture Notes in Computer Science},
abstract = {We present a new and comprehensive approach to inductive databases in the relational model. The main contribution is a new inductive query language extending SQL, with the goal of supporting the whole knowledge discovery process, from pre-processing via data mining to post-processing. A prototype system supporting the query language was developed in the SINDBAD (structured inductive database development) project. Setting aside models and focusing on distance-based and instance-based methods, closure can easily be achieved. An example scenario from the area of gene expression data analysis demonstrates the power and simplicity of the concept. We hope that this preliminary work will help to bring the fundamental issues, such as the integration of various pattern domains and data mining techniques, to the attention of the inductive database community.},
keywords = {data mining, inductive databases, machine learning, query languages},
pubstate = {published},
tppubtype = {inproceedings}
}
We present a new and comprehensive approach to inductive databases in the relational model. The main contribution is a new inductive query language extending SQL, with the goal of supporting the whole knowledge discovery process, from pre-processing via data mining to post-processing. A prototype system supporting the query language was developed in the SINDBAD (structured inductive database development) project. Setting aside models and focusing on distance-based and instance-based methods, closure can easily be achieved. An example scenario from the area of gene expression data analysis demonstrates the power and simplicity of the concept. We hope that this preliminary work will help to bring the fundamental issues, such as the integration of various pattern domains and data mining techniques, to the attention of the inductive database community.