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Sidorova, Yulia
Publications (8 of 8) Show all publications
Sidorova, Y., Rosander, O., Sköld, L., Grahn, H. & Lundberg, L. (2019). Finding a healthy equilibrium of geo-demographic segments for a telecom business: Who are malicious hot-spotters?. In: George A. Tsihrintzis, Dionisios N. Sotiropoulos, Lakhmi C. Jain (Ed.), Machine Learning Paradigms: Advances in Data Analytics (pp. 187-196). Springer Science and Business Media Deutschland GmbH
Open this publication in new window or tab >>Finding a healthy equilibrium of geo-demographic segments for a telecom business: Who are malicious hot-spotters?
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2019 (English)In: Machine Learning Paradigms: Advances in Data Analytics / [ed] George A. Tsihrintzis, Dionisios N. Sotiropoulos, Lakhmi C. Jain, Springer Science and Business Media Deutschland GmbH , 2019, p. 187-196Chapter in book (Refereed)
Abstract [en]

In telecommunication business, a major investment goes into the infrastructure and its maintenance, while business revenues are proportional to how big, good, and well-balanced the customer base is. In our previous work we presented a data-driven analytic strategy based on combinatorial optimization and analysis of the historical mobility designed to quantify the desirability of different geo-demographic segments, and several segments were recommended for a partial reduction. Within a segment, clients are different. In order to enable intelligent reduction, we introduce the term infrastructure-stressing client and, using the proposed method, we reveal the list of the IDs of such clients. We also have developed a visualization tool to allow for manual checks: it shows how the client moved through a sequence of hot spots and was repeatedly served by critically loaded antennas. The code and the footprint matrix are available on the SourceForge. © 2019, Springer International Publishing AG, part of Springer Nature.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2019
Series
Intelligent Systems Reference Library, ISSN 1868-4394 ; 149
Keywords
Business intelligence, Combinatorial optimization, Fuzzy logic, Geo-demographic segments, Mobility data, MOSAIC
National Category
Telecommunications Business Administration Computer Sciences
Identifiers
urn:nbn:se:bth-16885 (URN)10.1007/978-3-319-94030-4_8 (DOI)2-s2.0-85049522294 (Scopus ID)978-3-319-94029-8 (ISBN)
Available from: 2018-08-20 Created: 2018-08-20 Last updated: 2019-10-21Bibliographically approved
Sidorova, Y., Sköld, L., Rosander, O. & Lundberg, L. (2019). Optimizing utilization in cellular radio networks using mobility data. Optimization and Engineering, 20(1), 37-64
Open this publication in new window or tab >>Optimizing utilization in cellular radio networks using mobility data
2019 (English)In: Optimization and Engineering, ISSN 1389-4420, E-ISSN 1573-2924, Vol. 20, no 1, p. 37-64Article in journal (Refereed) Published
Abstract [en]

The main resource for any telecom operator is the physical radio cell network. We present two related methods for optimizing utilization in radio networks: Tetris optimization and selective cell expansion. Tetris optimization tries to find the mix of users from different market segments that provides the most even load in the network. Selective cell expansion identifies hotspot cells, expands the capacity of these radio cells, and calculates how many subscribers the radio network can handle after the expansions. Both methods are based on linear programming and use mobility data, i.e., data defining where different categories of subscribers tend to be during different times of the week. Based on real-world mobility data from a region in Sweden, we show that Tetris optimization based on six user segments made it possible to increase the number of subscribers by 58% without upgrading the physical infrastructure. The same data show that by selectively expanding less than 6% of the cells we are able to increase the number of subscribers by more than a factor of three without overloading the network. We also investigate the best way to combine Tetris optimization and selective cell expansion. © 2018 The Author(s)

Place, publisher, year, edition, pages
Springer New York LLC, 2019
Keywords
Cellular radio network, Linear programming, Mobility data, Optimization, Cytology, Radio, Cell expansion, Cellular radio networks, Hot-spot cells, Market segment, Mobility datum, Number of subscribers, Radio networks, Telecom operators, Cells
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-16334 (URN)10.1007/s11081-018-9387-4 (DOI)000457786900002 ()2-s2.0-85047379164 (Scopus ID)
Available from: 2018-06-07 Created: 2018-06-07 Last updated: 2019-02-21Bibliographically approved
Sidorova, Y., Sköld, L., Lennerstad, H. & Lundberg, L. (2019). The Use of Fuzzy Logic in Creating a Visual Data Summary of a Telecom Operator’s Customer Base. In: Communications in Computer and Information Science: . Paper presented at 1st International Conference on Intelligent Technologies and Applications, INTAP 2018; Bahawalpur; Pakistan; 23 October 2018 through 25 October 2018 (pp. 301-312). Springer Verlag, 932
Open this publication in new window or tab >>The Use of Fuzzy Logic in Creating a Visual Data Summary of a Telecom Operator’s Customer Base
2019 (English)In: Communications in Computer and Information Science, Springer Verlag , 2019, Vol. 932, p. 301-312Conference paper, Published paper (Refereed)
Abstract [en]

As pointed out by Zadeh, the mission of fuzzy logic in the era of big data is to create a relevant summary of huge amounts of data and facilitate decision-making. In this study, elements of fuzzy set theory are used to create a visual summary of telecom data, which gives a comprehensive idea concerning the desirability of boosting an operator’s presence in different neighborhoods and regions. The data used for validation cover historical mobility in a region of Sweden during a week. Fuzzy logic allows us to model inherently relative characteristics, such as “a tall man” or “a beautiful woman”, and importantly it also defines “anchors”, the situations (characterized with the value of the membership function for the characteristic) under which the relative notion receives a unique crisp interpretation. We propose color coding of the membership value for the relative notions such as “the desirability of boosting operator’s presence in the neighborhood” and “how well the operator is doing in the region”. The corresponding regions on the map (e.g., postcode zones or larger groupings) are colored in different shades passing from green (1) though yellow (0.5) to red (0). The color hues pass a clear intuitive message making the summary easy to grasp. © 2019, Springer Nature Singapore Pte Ltd.

Place, publisher, year, edition, pages
Springer Verlag, 2019
Series
Communications in Computer and Information Science, ISSN 1865-0929 ; 932
Keywords
Call Detail Records, Color, Fuzzy membership function, Mobility data, Computer circuits, Decision making, Fuzzy set theory, Membership functions, Color coding, Customerbase, Membership values, Mobility datum, Telecom operators, Use of fuzzy logic, Fuzzy logic
National Category
Telecommunications
Identifiers
urn:nbn:se:bth-17870 (URN)10.1007/978-981-13-6052-7_26 (DOI)000465006200026 ()9789811360510 (ISBN)
Conference
1st International Conference on Intelligent Technologies and Applications, INTAP 2018; Bahawalpur; Pakistan; 23 October 2018 through 25 October 2018
Available from: 2019-05-02 Created: 2019-05-02 Last updated: 2019-05-03Bibliographically approved
Podapati, S., Lundberg, L., Sköld, L., Rosander, O. & Sidorova, Y. (2017). Fuzzy recommendations in marketing campaigns. In: Darmont J.,Kirikova M.,Norvag K.,Wrembel R.,Papadopoulos G.A.,Gamper J.,Rizzi S. (Ed.), Communications in Computer and Information Science: . Paper presented at 21st European Conference on Advances in Databases and Information Systems, ADBIS, Nicosia (pp. 246-256). , 767
Open this publication in new window or tab >>Fuzzy recommendations in marketing campaigns
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2017 (English)In: Communications in Computer and Information Science / [ed] Darmont J.,Kirikova M.,Norvag K.,Wrembel R.,Papadopoulos G.A.,Gamper J.,Rizzi S., 2017, Vol. 767, p. 246-256Conference paper, Published paper (Refereed)
Abstract [en]

The population in Sweden is growing rapidly due to immigration. In this light, the issue of infrastructure upgrades to provide telecommunication services is of importance. New antennas can be installed at hot spots of user demand, which will require an investment, and/or the clientele expansion can be carried out in a planned manner to promote the exploitation of the infrastructure in the less loaded geographical zones. In this paper, we explore the second alternative. Informally speaking, the term Infrastructure-Stressing describes a user who stays in the zones of high demand, which are prone to produce service failures, if further loaded. We have studied the Infrastructure-Stressing population in the light of their correlation with geo-demographic segments. This is motivated by the fact that specific geo-demographic segments can be targeted via marketing campaigns. Fuzzy logic is applied to create an interface between big data, numeric methods for its processing, and a manager who wants a comprehensible summary. © 2017, Springer International Publishing AG.

Keywords
Call detail records, Fuzzy membership function, Geo-demographic segments, Intelligent data mining, Marketing
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-15307 (URN)10.1007/978-3-319-67162-8_24 (DOI)2-s2.0-85029801359 (Scopus ID)9783319671611 (ISBN)
Conference
21st European Conference on Advances in Databases and Information Systems, ADBIS, Nicosia
Available from: 2017-10-06 Created: 2017-10-06 Last updated: 2018-01-13Bibliographically approved
Sidorova, Y., Lundberg, L., Rosander, O., Grahn, H. & Skold, L. (2017). Recommendations for marketing campaigns in telecommunication business based on the footprint analysis: Who is a good client?. In: 2017 8th International Conference on Information, Intelligence, Systems and Applications, IISA 2017: . Paper presented at 8th International Conference on Information, Intelligence, Systems and Applications, IISA 2017, Larnaca (pp. 513-518). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Recommendations for marketing campaigns in telecommunication business based on the footprint analysis: Who is a good client?
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2017 (English)In: 2017 8th International Conference on Information, Intelligence, Systems and Applications, IISA 2017, Institute of Electrical and Electronics Engineers Inc. , 2017, p. 513-518Conference paper, Published paper (Refereed)
Abstract [en]

A major investment made by a telecom operator goes into the infrastructure and its maintenance, while business revenues depend on how efficiently it is exploited. We present a data-driven analytic strategy based on combinatorial optimization and analysis of historical data. The data cover historical mobility in one region of Sweden during a week. Applying the proposed method in a case study, we have identified the optimal combination of geodemographic segments in the customer base, developed a functionality to assess the potential of a planned marketing campaign, and investigated how many and which segments to target for customer base growth. A comprehensible summary of the conclusions is created via execution of the queries with a fuzzy logic component. © 2017 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2017
Keywords
business intelligence, combinatorial optimization, fuzzy logic, geodemographic segments, mobility data, MOSAIC, Commerce, Competitive intelligence, Computer circuits, Investments, Footprint analysis, Historical data, Marketing campaign, Mobility datum, Optimal combination, Telecom operators, Marketing
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-16539 (URN)10.1109/IISA.2017.8316396 (DOI)000454859600092 ()2-s2.0-85047937690 (Scopus ID)9781538637319 (ISBN)
Conference
8th International Conference on Information, Intelligence, Systems and Applications, IISA 2017, Larnaca
Available from: 2018-06-18 Created: 2018-06-18 Last updated: 2019-06-27Bibliographically approved
Sagar, S., Skold, L., Lundberg, L. & Sidorova, Y. (2017). Trajectory Segmentation for a Recommendation Module of a Customer Relationship Management System. In: Wu, Y Min, G Georgalas, N AlDubi, A Jin, X Yang, L Ma, J Yang, P (Ed.), Proceedings - 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017: . Paper presented at EEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Exeter (pp. 1150-1155). IEEE
Open this publication in new window or tab >>Trajectory Segmentation for a Recommendation Module of a Customer Relationship Management System
2017 (English)In: Proceedings - 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017 / [ed] Wu, Y Min, G Georgalas, N AlDubi, A Jin, X Yang, L Ma, J Yang, P, IEEE , 2017, p. 1150-1155Conference paper, Published paper (Refereed)
Abstract [en]

In business analytics some industries rely heavily on commercial geo-demographic segmentation systems (MOSAIC, ACORN, etc.), which are a universally strong predictor of user's behavior: from diabetes propensity and purchasing habits to political preferences. A segment is defined with a postcode of the client's home address. Recent research suggests that a mature competitor to geo-demographic segmentation is about to emerge: segmentation based on user mobility is reported to be a reliable proxy of social well-being of the neighborhood. In this submission, we have completed a user segmentation model based on clustering of user trajectories from the Call Detail Records covering one week of activity of one region in Sweden. The new segmentation has been compared against MOSAIC in the recommendation module of a customer relationship management system and has revealed better business options with regard to network exploitation and potential revenues. The implementation is available from the corresponding author (JS or LL) on request.

Place, publisher, year, edition, pages
IEEE, 2017
Keywords
trajectory clustering, user segmentation, spectral clustering
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-16057 (URN)10.1109/iThings-GreenCom-CPSCom-SmartData.2017.177 (DOI)000426972400177 ()
Conference
EEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Exeter
Available from: 2018-04-04 Created: 2018-04-04 Last updated: 2018-07-10Bibliographically approved
Sagar, S. & Sidorova, Y. (2016). Sequence retriever for known, discovered, and user-specified molecular fragments. In: Fdez-Riverola F.,De Paz J.F.,Rocha M.P.,Mayo F.J.D.,Mohamad M.S. (Ed.), 10TH INTERNATIONAL CONFERENCE ON PRACTICAL APPLICATIONS OF COMPUTATIONAL BIOLOGY & BIOINFORMATICS: . Paper presented at International Conference on Practical Applications of Computational Biology and Bioinformatics (PACBB), Sevilla; Spain (pp. 51-58). Springer, 477
Open this publication in new window or tab >>Sequence retriever for known, discovered, and user-specified molecular fragments
2016 (English)In: 10TH INTERNATIONAL CONFERENCE ON PRACTICAL APPLICATIONS OF COMPUTATIONAL BIOLOGY & BIOINFORMATICS / [ed] Fdez-Riverola F.,De Paz J.F.,Rocha M.P.,Mayo F.J.D.,Mohamad M.S., Springer, 2016, Vol. 477, p. 51-58Conference paper, Published paper (Refereed)
Abstract [en]

Typically, biological and chemical data are sequential, for example, as in genomic sequences or as in diverse chemical formats, such as InChI or SMILES. That poses a major problem for computational analysis, since the majority of the methods for data mining and prediction were developed to work on feature vectors. To address this challenge, a functionality of a Statistical Adapter has been proposed recently. It automatically converts parsable sequential input into feature vectors. During the conversion, insights are gained into the problem via finding regions of interest in the sequence and the level of abstraction for their representation, and the feature vectors are filled with the counts of interesting sequence fragments,-finally, making it possible to benefit from powerful vectorbased methods. For this submission, the Sequence Retriever has been added to the Adapter. While the Adapter performs the conversion: sequence → vector with the counts of interesting molecular fragments, the Retriever performs the mapping: molecular fragment → sequences from the database that contain this fragment.

Place, publisher, year, edition, pages
Springer, 2016
Series
Advances in Intelligent Systems and Computing, ISSN 2194-5357 ; 477
Keywords
Bioactivity; Bioinformatics; Data mining, Computational analysis; Genomic sequence; Level of abstraction; Molecular fragments; Parsing; Regions of interest; Sequence retrieval; Vector-based methods, Vectors
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:bth-13193 (URN)10.1007/978-3-319-40126-3_6 (DOI)000389591300006 ()2-s2.0-84976340337 (Scopus ID)9783319401256 (ISBN)
Conference
International Conference on Practical Applications of Computational Biology and Bioinformatics (PACBB), Sevilla; Spain
Available from: 2016-10-03 Created: 2016-10-03 Last updated: 2018-01-14Bibliographically approved
Sidorova, Y. & Garcia, J. (2015). Bridging from syntactic to statistical methods: Classification with automatically segmented features from sequences. Pattern Recognition, 48(11), 3749-3756
Open this publication in new window or tab >>Bridging from syntactic to statistical methods: Classification with automatically segmented features from sequences
2015 (English)In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 48, no 11, p. 3749-3756Article in journal (Refereed) Published
Abstract [en]

To integrate the benefits of statistical methods into syntactic pattern recognition, a Bridging Approach is proposed: (i) acquisition of a grammar per recognition class; (ii) comparison of the obtained grammars in order to find substructures of interest represented as sequences of terminal and/or non-terminal symbols and filling the feature vector with their counts; (iii) hierarchical feature selection and hierarchical classification, deducing and accounting for the domain taxonomy. The bridging approach has the benefits of syntactic methods: preserves structural relations and gives insights into the problem. Yet, it does not imply distance calculations and, thus, saves a non-trivial task-dependent design step. Instead it relies on statistical classification from many features. Our experiments concern a difficult problem of chemical toxicity prediction. The code and the data set are open-source. (C) 2015 Elsevier Ltd. All rights reserved.

Keywords
Syntactic pattern recognition, Grammatical inference, Feature segmentation, SMILES parser, Feature extraction
National Category
Computer Systems
Identifiers
urn:nbn:se:bth-10555 (URN)10.1016/j.patcog.2015.05.001 (DOI)000359028900037 ()
Available from: 2015-09-15 Created: 2015-09-14 Last updated: 2017-12-04Bibliographically approved
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