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Ågren, Ola M
Alternative names
Publications (9 of 9) Show all publications
Ågren, O. (2015). AMBiDDS: A system for Automatic Mining of BIg Discrete Data-Sets. In: 2015 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL INTELLIGENCE (CSCI): . Paper presented at International Conference on Computational Science and Computational Intelligence (CSCI), DEC 07-09, 2015, Las Vegas, NV (pp. 424-427).
Open this publication in new window or tab >>AMBiDDS: A system for Automatic Mining of BIg Discrete Data-Sets
2015 (English)In: 2015 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL INTELLIGENCE (CSCI), 2015, p. 424-427Conference paper, Published paper (Refereed)
Abstract [en]

This paper introduces an automatic algorithm that can be seen as an extension to the Eclat algorithm, as well as a corresponding proof of concept prototype. It uses inverted indices and statistical pruning of the possible solution space as early as possible.

Keywords
Data mining, inverted indices, statistical pruning
National Category
Computational Mathematics
Identifiers
urn:nbn:se:umu:diva-124694 (URN)10.1109/CSCI.2015.142 (DOI)000380405100076 ()978-1-4673-9795-7 (ISBN)
Conference
International Conference on Computational Science and Computational Intelligence (CSCI), DEC 07-09, 2015, Las Vegas, NV
Available from: 2016-10-28 Created: 2016-08-22 Last updated: 2018-06-09Bibliographically approved
Ågren, O. M. (2015). Student-graded oral presentations. International Journal of Engineering Pedagogy, 5(4), 76-78
Open this publication in new window or tab >>Student-graded oral presentations
2015 (English)In: International Journal of Engineering Pedagogy, ISSN 2192-4880, Vol. 5, no 4, p. 76-78Article in journal (Refereed) Published
Abstract [en]

We describe a way to use peer-graded oral presentations as a way of reducing the load on the teacher, and show that almost identical results as can be achieved as with teacher graded presentations. Moreover, we have found that very little in the form of explicit criteria are needed.

Place, publisher, year, edition, pages
Kassel University Press GmbH, 2015
Keywords
Didactics, Peer assessment, Teacher offloading
National Category
Educational Sciences
Identifiers
urn:nbn:se:umu:diva-114390 (URN)10.3991/ijep.v5i4.4841 (DOI)000366993800009 ()
Available from: 2016-01-18 Created: 2016-01-18 Last updated: 2018-06-07Bibliographically approved
Ågren, O. M. (2012). The ProT Nordic Web Dataset. In: Hamid R. Arabnia, Victor A. Clincy, Leonidas Deligiannidis, Andy Marsh, Ashu M. G. Solo (Ed.), Proceedings of the International Conference on Internet Computing: ICOMP 2012. Paper presented at The 2012 International Conference on Internet Computing (ICOMP'12) (pp. 125-128). Las Vegas, Nevada: CSREA Press
Open this publication in new window or tab >>The ProT Nordic Web Dataset
2012 (English)In: Proceedings of the International Conference on Internet Computing: ICOMP 2012 / [ed] Hamid R. Arabnia, Victor A. Clincy, Leonidas Deligiannidis, Andy Marsh, Ashu M. G. Solo, Las Vegas, Nevada: CSREA Press, 2012, p. 125-128Conference paper, Oral presentation only (Refereed)
Abstract [en]

In this paper we present a free dataset, usable for testing web search engines.  The dataset corresponds to a snapshot of the Nordic part of the Internet in early 2007 and is highly abstracted, with numbers representing each web page.  The released dataset consists of three parts; a graph, 76 sets of pages containing each tested word combination, and some files to use when calculating relevance of the resulting sets of algorithms/search engines. We also present a new compound statistic as well as statistical results for some search engine and information retrieval algorithms.

Place, publisher, year, edition, pages
Las Vegas, Nevada: CSREA Press, 2012
Keywords
Nordic Web Dataset, Search Engine Evaluation, Relevance Metrics
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-64012 (URN)1-60132-220-8 (ISBN)
Conference
The 2012 International Conference on Internet Computing (ICOMP'12)
Projects
ProT
Available from: 2013-01-11 Created: 2013-01-11 Last updated: 2018-06-08Bibliographically approved
Ågren, O. (2011). Using the ProT Nordic Web Dataset.
Open this publication in new window or tab >>Using the ProT Nordic Web Dataset
2011 (English)Report (Other academic)
Abstract [en]

In this paper we present a free dataset, usable for testing web search engines. The dataset corresponds to a snapshot of the Nordic part of the Internet back in early 2007 and is highly abstracted, with numbers representing each web page. The released dataset consists of three parts; a graph, 76 sets of pages containing each tested word combination, and some files to use when calculating relevance of the resulting sets of algorithms/search engines. We also present statistics for some search engine algorithms.

Publisher
p. 29
Series
Report / UMINF, ISSN 0348-0542 ; 13
Keywords
Nordic Web Dataset, Search Engine Evaluation, Relevance Metrics
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
computer and systems sciences
Identifiers
urn:nbn:se:umu:diva-49307 (URN)
Projects
ProT
Available from: 2011-11-10 Created: 2011-11-07 Last updated: 2018-06-08Bibliographically approved
Ågren, O. (2008). S²ProT: Rank Allocation by Superpositioned Propagation of Topic-Relevance. International Journal of Web Information Systems, 4(4), 416-440
Open this publication in new window or tab >>S²ProT: Rank Allocation by Superpositioned Propagation of Topic-Relevance
2008 (English)In: International Journal of Web Information Systems, ISSN 1744-0084, Vol. 4, no 4, p. 416-440Article in journal (Refereed) Published
Abstract [en]

Purpose – The purpose of this paper is to assign topic-specific ratings to web pages.

Design/methodology/approach – The paper uses power iteration to assign topic-specific rating values (called relevance) to web pages, creating a ranking or partial order among these pages for each topic. This approach depends on a set of pages that are initially assumed to be relevant for a specific topic; the spatial link structure of the web pages; and a net-specific decay factor designated ξ.

Findings – The paper finds that this approach exhibits desirable properties such as fast convergence, stability and yields relevant answer sets. The first property will be shown using theoretical proofs, while the others are evaluated through stability experiments and assessments of real world data in comparison with already established algorithms.

Research limitations/implications – In the assessment, all pages that a web spider was able to find in the Nordic countries were used. It is also important to note that entities that use domains outside the Nordic countries (e.g..com or.org) are not present in the paper's datasets even though they reside logically within one or more of the Nordic countries. This is quite a large dataset, but still small in comparison with the entire worldwide web. Moreover, the execution speed of some of the algorithms unfortunately prohibited the use of a large test dataset in the stability tests.

Practical implications – It is not only possible, but also reasonable, to perform ranking of web pages without using Markov chain approaches. This means that the work of generating answer sets for complex questions could (at least in theory) be divided into smaller parts that are later summed up to give the final answer.

Originality/value – This paper contributes to the research on internet search engines.

Keywords
Information retrieval, Search engines, Spatial data structures, Worldwide web
National Category
Computer Sciences
Identifiers
urn:nbn:se:umu:diva-11236 (URN)10.1108/17440080810919477 (DOI)
Available from: 2008-12-01 Created: 2008-12-01 Last updated: 2018-06-09Bibliographically approved
Ågren, O. (2006). Assessment of WWW-Based Ranking Systems for Smaller Web Sites. INFOCOMP Journal of Computer Science, 5(2), 45-55
Open this publication in new window or tab >>Assessment of WWW-Based Ranking Systems for Smaller Web Sites
2006 (English)In: INFOCOMP Journal of Computer Science, ISSN 1807-4545, Vol. 5, no 2, p. 45-55Article in journal (Refereed) Published
Abstract [en]

A comparison between a number of search engines from three different families (HITS, PageRank, and Propagation of Trust) is presented for a small web server with respect to perceived relevance. A total of 307 individual tests have been done and the results from these were disseminated to the algorithms, and then handled using confidence intervals, Kolmogorov-Smirnov and ANOVA. We show that the results can be grouped according to algorithm family, and also that the algorithms (or at least families) can be partially ordered in order of relevance.

Keywords
Assessment, Search engines, HITS, PageRank, Propagation of Trust, eigenvectors
National Category
Computer Sciences
Identifiers
urn:nbn:se:umu:diva-8415 (URN)
Available from: 2008-01-21 Created: 2008-01-21 Last updated: 2018-06-09Bibliographically approved
Ågren, O. (2003). CHiC: A Fast Concept Hierarchy Constructor for Discrete or Mixed Mode Databases. In: SEKE 2003: Proceedings of the Fifteenth International Conference on Software Engineering & Knowledge Engineering. Paper presented at The 15th International Conference on Software Engineering and Knowledge Engineering (SEKE'03), San Fransisco, July 1-3, 2003 (pp. 250-258). Knowledge Systems Institute
Open this publication in new window or tab >>CHiC: A Fast Concept Hierarchy Constructor for Discrete or Mixed Mode Databases
2003 (English)In: SEKE 2003: Proceedings of the Fifteenth International Conference on Software Engineering & Knowledge Engineering, Knowledge Systems Institute, 2003, p. 250-258Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we propose an algorithm that automatically creates concept hierarchies or lattices for discrete databases and datasets. The reason for doing this is to accommodate later data mining operations on the same sets of data without having an expert create these hierarchies by hand.

Each step of the algorithm will be examined; We will show inputs and output for each step using a small example. The theoretical upper bound of the complexity for each part of the algorithm will be presented, as well as real time measurements for a number of databases. We will finally present a time model of the algorithm in terms of a number of attributes of the databases

Place, publisher, year, edition, pages
Knowledge Systems Institute, 2003
Keywords
Data Mining, Data Preprocessing, Hierarchy Generation, Lattice Generation
National Category
Computer Sciences
Research subject
business data processing
Identifiers
urn:nbn:se:umu:diva-22348 (URN)1891706128 (ISBN)
Conference
The 15th International Conference on Software Engineering and Knowledge Engineering (SEKE'03), San Fransisco, July 1-3, 2003
Projects
CHiC
Available from: 2009-05-06 Created: 2009-05-06 Last updated: 2018-06-08Bibliographically approved
Ågren, O. (2002). Automatic Generation of Concept Hierarchies for a Discrete Data Mining System. In: Hamid R. Arabnia, Youngsong Mun, Bhanu Prasad (Ed.), International Conference on Information and Knowledge Engineering (IKE '02): . Paper presented at The 2002 International Conference on Information and Knowledge Engineering (IKE '02), June 24-27, 2002, Las Vegas, USA (pp. 287-293). CSREA Press
Open this publication in new window or tab >>Automatic Generation of Concept Hierarchies for a Discrete Data Mining System
2002 (English)In: International Conference on Information and Knowledge Engineering (IKE '02) / [ed] Hamid R. Arabnia, Youngsong Mun, Bhanu Prasad, CSREA Press, 2002, p. 287-293Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we propose an algorithm for automatic creation of concept hierarchies from discrete databases and datasets. The reason for doing this is to accommodate later data mining operations on the same set of data without having an expert create these hierachies by hand.

We will go through the algorithm thoroughly and show the results from each step of the algorithm using a (small) example. We will also give actual execution times for our prototype for non-trivial example data sets and estimates of the complexity of the algorithm in terms of the number of records and the number of distinct data values in the data set.

Place, publisher, year, edition, pages
CSREA Press, 2002
Keywords
Data Mining, Data Preprocessing, Hierarchy Generation
National Category
Computer Sciences
Research subject
business data processing
Identifiers
urn:nbn:se:umu:diva-22344 (URN)1892512971 (ISBN)
Conference
The 2002 International Conference on Information and Knowledge Engineering (IKE '02), June 24-27, 2002, Las Vegas, USA
Projects
CHiC
Available from: 2009-05-06 Created: 2009-05-06 Last updated: 2018-06-08Bibliographically approved
Ågren, O. (2001). AlgExt: an Algorithm Extractor for C Programs. Umeå: Department of Computing Science, Umeå University
Open this publication in new window or tab >>AlgExt: an Algorithm Extractor for C Programs
2001 (English)Report (Other academic)
Abstract [en]

ALGEXT is a program that extracts strategic/block comments from C source files to improve maintainability and to keep documentation consistent with source code. This is done by writing the comments in the source code in what we call extractable algorithms, describing the algorithm used in the functions.

ALGEXT recognizes different kinds of comments:

  • Strategic comments are comments that proceed a block of code, with only whitespace preceding it on the line,
  • Tactical comments are comments that describes the code that precedes it on the same line,
  • Function comments are comments immediately preceding a function definition, describing the function,
  • File comments are comments at the head of the file, before any declarations of functions and variables, and finally
  • Global comments are comments within the global scope, but not associated with a function.

Only strategic comment are used as basis for algorithm extraction in ALGEXT.

The paper discusses the rationale for ALGEXT and describes its implementation and usage. Examples are presented for clarification of what can be done with ALGEXT.

Our experience shows that students who use ALGEXT for preparing theirassignments tend to write about 66% more comments than non-ALGEXT users.

Place, publisher, year, edition, pages
Umeå: Department of Computing Science, Umeå University, 2001. p. 15
Series
Report / UMINF, ISSN 0348-0542 ; 2001:11
Keywords
Extractable algorithms, Embedded information, C
National Category
Computer Sciences
Research subject
business data processing
Identifiers
urn:nbn:se:umu:diva-22350 (URN)
Distributor:
Institutionen för datavetenskap, 90187, Umeå
Projects
AlgExt
Available from: 2009-05-06 Created: 2009-05-06 Last updated: 2018-06-08Bibliographically approved
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