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Journal articles on the topic 'Logical data'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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1

Kuper, Gabriel M., and Moshe Y. Vardi. "The logical data model." ACM Transactions on Database Systems 18, no. 3 (September 1993): 379–413. http://dx.doi.org/10.1145/155271.155274.

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2

Segev, Arie, and Arie Shoshani. "Logical modeling of temporal data." ACM SIGMOD Record 16, no. 3 (December 1987): 454–66. http://dx.doi.org/10.1145/38714.38760.

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3

Garani, Georgia, George K. Adam, and Dimitrios Ventzas. "Temporal data warehouse logical modelling." International Journal of Data Mining, Modelling and Management 8, no. 2 (2016): 144. http://dx.doi.org/10.1504/ijdmmm.2016.077156.

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4

Boros, Endre, Peter L. Hammer, Toshihide Ibaraki, and Alexander Kogan. "Logical analysis of numerical data." Mathematical Programming 79, no. 1-3 (October 1997): 163–90. http://dx.doi.org/10.1007/bf02614316.

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5

Grau, Bernardo Cuenca, and Egor V. Kostylev. "Logical Foundations of Linked Data Anonymisation." Journal of Artificial Intelligence Research 64 (February 16, 2019): 253–314. http://dx.doi.org/10.1613/jair.1.11355.

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The widespread adoption of the Linked Data paradigm has been driven by the increasing demand for information exchange between organisations, as well as by regulations in domains such as health care and governance that require certain data to be published. In this setting, sensitive information is at high risk of disclosure since published data can be often seamlessly linkedwith arbitrary external data sources.In this paper we lay the logical foundations of anonymisation in the context of Linked Data. We consider anonymisations of RDF graphs (and, more generally, relational datasets with labelled nulls) and define notions of policy-compliant and linkage-safe anonymisations. Policy compliance ensures that an anonymised dataset does not reveal any sensitive information as specified by a policy query. Linkage safety ensures that an anonymised dataset remains compliant even if it is linked to (possibly unknown) external datasets available on the Web, thus providing provable protection guarantees against data linkage attacks. We establish the computational complexity of the underpinning decision problems both under the open-world semantics inherent to RDF and under the assumption that an attacker has complete, closed-world knowledge over some parts of the original data.
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6

Ekin, Oya, Peter L. Hammer, and Alexander Kogan. "Convexity and logical analysis of data." Theoretical Computer Science 244, no. 1-2 (August 2000): 95–116. http://dx.doi.org/10.1016/s0304-3975(98)00337-5.

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7

Prokoshyna, Nataliya, Jaroslaw Szlichta, Fei Chiang, Renée J. Miller, and Divesh Srivastava. "Combining quantitative and logical data cleaning." Proceedings of the VLDB Endowment 9, no. 4 (December 2015): 300–311. http://dx.doi.org/10.14778/2856318.2856325.

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8

Turpin, Russell. "A logical approach to data structures." ACM SIGSOFT Software Engineering Notes 18, no. 5 (December 1993): 138–48. http://dx.doi.org/10.1145/167049.167073.

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9

Bouyer, Patricia. "A logical characterization of data languages." Information Processing Letters 84, no. 2 (October 2002): 75–85. http://dx.doi.org/10.1016/s0020-0190(02)00229-6.

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10

Barceló, Pablo. "Logical foundations of relational data exchange." ACM SIGMOD Record 38, no. 1 (June 24, 2009): 49–58. http://dx.doi.org/10.1145/1558334.1558341.

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11

HADZILACOS, THANASIS, and NECTARIA TRYFONA†. "Logical data modelling for geographical applications." International journal of geographical information systems 10, no. 2 (March 1996): 179–203. http://dx.doi.org/10.1080/02693799608902073.

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12

Hadzilacos, Thanasis, and Nectaria Tryfona. "Logical data modelling for geographical applications." Geographical Information Systems 10, no. 2 (March 1, 1996): 179–203. http://dx.doi.org/10.1080/026937996138115.

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13

PFALTZ, JOHN L. "ESTABLISHING LOGICAL RULES FROM EMPIRICAL DATA." International Journal on Artificial Intelligence Tools 17, no. 05 (October 2008): 985–1001. http://dx.doi.org/10.1142/s0218213008004254.

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We review a method of generating logical rules, or axioms, from empirical data. This method, using closed set properties of formal concept analysis, has been previously described and tested on rather large sets of deterministic data. In spite of the fact that formal concept techniques have been used to prune frequent set data mining results, frequency and/or statistical significance are totally irrelevant to this method. It is strictly logical and deterministic. The contribution of this paper is a completely new extension of this method to create implications involving numeric inequalities. That is, numerical inequalities such as "age > 39" can be treated as logical predicates that have been extracted from the data itself and not postulated apriori.
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14

Pangalos, G. "Logical design of data base systems." Information & Management 17, no. 1 (August 1989): 23–29. http://dx.doi.org/10.1016/0378-7206(89)90052-9.

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15

Thornham, Helen, and Edgar Gómez Cruz. "Hackathons, data and discourse: Convolutions of the data (logical)." Big Data & Society 3, no. 2 (December 2016): 205395171667967. http://dx.doi.org/10.1177/2053951716679675.

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16

Novikov, Kirill E. "Logical presuppositions of the survey data distortion." Sociological Journal, no. 4 (2013): 72–88. http://dx.doi.org/10.19181/socjour.2013.4.438.

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17

Ono, Hirotaka, Kazuhisa Makino, and Toshihide Ibaraki. "Logical analysis of data with decomposable structures." Theoretical Computer Science 289, no. 2 (October 2002): 977–95. http://dx.doi.org/10.1016/s0304-3975(01)00413-3.

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18

Chauhan, Himani, Garima Saxena, and Arpit Tripathi. "Logical Analysis of Data-A Survey Paper." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 3 (March 30, 2017): 227–29. http://dx.doi.org/10.23956/ijarcsse/v7i3/0177.

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19

Lyutikova, Larisa. "Logical Analysis of Data for outliers detection." Procedia Computer Science 169 (2020): 330–36. http://dx.doi.org/10.1016/j.procs.2020.02.192.

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20

Boros, E., P. L. Hammer, T. Ibaraki, A. Kogan, E. Mayoraz, and I. Muchnik. "An implementation of logical analysis of data." IEEE Transactions on Knowledge and Data Engineering 12, no. 2 (March 2000): 292–306. http://dx.doi.org/10.1109/69.842268.

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21

Samba, Augustine. "Logical Data Models for Cloud Computing Architectures." IT Professional 14, no. 1 (January 2012): 19–26. http://dx.doi.org/10.1109/mitp.2011.113.

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22

Boros, Endre, Yves Crama, Peter L. Hammer, Toshihide Ibaraki, Alexander Kogan, and Kazuhisa Makino. "Logical analysis of data: classification with justification." Annals of Operations Research 188, no. 1 (July 9, 2011): 33–61. http://dx.doi.org/10.1007/s10479-011-0916-1.

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23

Bakshi, Waseem Jeelani, Rana Hashmy, Majid Zaman, and Muheet Ahmed Butt. "Logical Data Integration Model for the Integration of Data Repositories." International Journal of Database Theory and Application 11, no. 1 (March 31, 2018): 21–28. http://dx.doi.org/10.14257/ijdta.2018.11.1.03.

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24

Gargano, M., E. Nardelli, and M. Talamo. "Abstract data types for the logical modeling of complex data." Information Systems 16, no. 6 (January 1991): 565–83. http://dx.doi.org/10.1016/0306-4379(91)90020-a.

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25

Grice, James W., and Mark Hopper. "Reliability of Coordinate Grid Data." Psychological Reports 91, no. 3_suppl (December 2002): 1235–43. http://dx.doi.org/10.2466/pr0.2002.91.3f.1235.

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The reliability of data obtained from a modified repertory grid procedure was examined. The coordinate grid, developed by Chambers (1983), measures the logical inconsistency and integrative complexity of an individual's view of different people. 56 college students completed two forms of the coordinate grid on each of two testing occasions. Analysis indicated low reliability for measures of both logical inconsistency ( r = −.04) and integrative complexity ( r = .31) with respect to the people entered into the grid. The test-retest results were better ( r = .51 for logical inconsistency; r = .35 for integrative complexity) but still well below acceptable reliability. Hence, generalizations beyond a particular grid may be unwarranted.
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26

Noviani, Julia, Hilda Hakim, and Jarwandi Jarwandi. "ANALISIS KEMAMPUAN BERPIKIR LOGIS PADA MATERI PELUANG DI KELAS IX SMP NEGERI 1 TAKENGON." Jurnal Ilmiah Pendidikan Matematika Al Qalasadi 4, no. 1 (June 30, 2020): 14–23. http://dx.doi.org/10.32505/qalasadi.v4i1.1604.

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This study aims to describe the ability of students to think logically on probability subject in class IX in SMP Negeri 1 Takengon. This study uses a qualitative approach to the type of descriptive research. The subject of this study consisted of three students, with the criteria of one student with high ability, one with moderate ability, and one with low ability. Data collection used tests and semi-structured interviews. The instrument used was a test of probabality material. The process of data analysis in this study includes data reduction, data presentation, and drawing conclusions. Based on the analysis of the description of students logical thinking abilities with high criteria (student 1) showed excellent logical thinking skills. Then students with moderate abilities (student 2) showed good logical thinking skills. Student 2 has a deficiency in the stage of thought twitching. While students with low criteria (student 3) demonstrate sufficient logical thinking skills. Student 3 has a deficiency in the stage of thought twitching and drawing conclusions that indicate the student's inaccuracy. Overall the ability to think logically these three students can be said to be good. This can be seen from every work and interview results,, students are able to do most of the indicators of logical thinking ability.
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27

Martin, Bryan, and Karen C. Davis. "Multi-Temperate Logical Data Warehouse Design for Large-Scale Healthcare Data." Big Data Research 25 (July 2021): 100255. http://dx.doi.org/10.1016/j.bdr.2021.100255.

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28

Bedel, Olivier, Sébastien Ferré, Olivier Ridoux, and Erwan Quesseveur. "GEOLIS: a logical information system for geographical data." Revue internationale de géomatique 17, no. 3-4 (December 30, 2007): 371–90. http://dx.doi.org/10.3166/geo.17.371-390.

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29

Fierens, Daan. "Learning directed probabilistic logical models from relational data." AI Communications 21, no. 4 (2008): 269–70. http://dx.doi.org/10.3233/aic-2008-0428.

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30

Boros, Endre, Toshihide Ibaraki, and Kazuhisa Makino. "Logical analysis of binary data with missing bits." Artificial Intelligence 107, no. 2 (February 1999): 219–63. http://dx.doi.org/10.1016/s0004-3702(98)00110-6.

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31

Wa̧siewicz, Piotr, Artur Malinowski, Robert Nowak, Jan J. Mulawka, Piotr Borsuk, Piotr Wȩgleński, and Andrzej Płucienniczak. "DNA computing: implementation of data flow logical operations." Future Generation Computer Systems 17, no. 4 (January 2001): 361–78. http://dx.doi.org/10.1016/s0167-739x(99)00117-x.

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32

Чередніченко, О. Ю., Ю. М. Гонтар, А. В. Василенко, and О. М. Матвєєв. "Business data processing based on algebra-logical models." Системи обробки інформації, no. 4(150) (May 26, 2017): 163–69. http://dx.doi.org/10.30748/soi.2017.150.35.

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33

Lyutikova, Larisa, and Elena Shmatova. "Using a logical derivative to analyze data outlier." Procedia Computer Science 169 (2020): 304–9. http://dx.doi.org/10.1016/j.procs.2020.02.187.

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34

Johnson, Michael, and Robert Rosebrugh. "Database interoperability through state-based logical data independence." International Journal of Computer Applications in Technology 16, no. 2/3 (2003): 97. http://dx.doi.org/10.1504/ijcat.2003.000316.

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35

Fan, Tuan g., Churn Jung Liau, Tsau Young Lin, and Karen Lee. "A logical formulation of the granular data model." International Journal of Granular Computing, Rough Sets and Intelligent Systems 1, no. 3 (2010): 289. http://dx.doi.org/10.1504/ijgcrsis.2010.029583.

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36

Khrisat, Dr Mohammad S., and Ziad A. Alqadi. "Using Logical Operations to Secure LSB2 Data Steganography." International Journal of Computer Science and Mobile Computing 9, no. 11 (November 30, 2020): 70–76. http://dx.doi.org/10.47760/ijcsmc.2020.v09i11.007.

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37

Campbell, K. E., A. K. Das, and M. A. Musen. "A Logical Foundation for Representation of Clinical Data." Journal of the American Medical Informatics Association 1, no. 3 (May 1, 1994): 218–32. http://dx.doi.org/10.1136/jamia.1994.95236154.

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38

Derrar, Hacène, Mohamed Ahmed Nacer, and Omar Boussaid. "Particle swarm optimisation for data warehouse logical design." International Journal of Bio-Inspired Computation 4, no. 4 (2012): 249. http://dx.doi.org/10.1504/ijbic.2012.048072.

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39

Bain, Travaughn C., Juan F. Avila-Herrera, Ersoy Subasi, and Munevver Mine Subasi. "Logical analysis of multiclass data with relaxed patterns." Annals of Operations Research 287, no. 1 (September 25, 2019): 11–35. http://dx.doi.org/10.1007/s10479-019-03389-7.

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40

Alexe, Gabriela, and Peter L. Hammer. "Spanned patterns for the logical analysis of data." Discrete Applied Mathematics 154, no. 7 (May 2006): 1039–49. http://dx.doi.org/10.1016/j.dam.2005.03.031.

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41

Hammer, Peter L., Alexander Kogan, Bruno Simeone, and Sándor Szedmák. "Pareto-optimal patterns in logical analysis of data." Discrete Applied Mathematics 144, no. 1-2 (November 2004): 79–102. http://dx.doi.org/10.1016/j.dam.2003.08.013.

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42

Ono, Hirotaka, Mutsunori Yagiura, and Toshihide Ibaraki. "A decomposability index in logical analysis of data." Discrete Applied Mathematics 142, no. 1-3 (August 2004): 165–80. http://dx.doi.org/10.1016/j.dam.2004.02.001.

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43

Jenkins, Michael A., and Janice I. Glasgow. "A logical basis for nested array data structures." Computer Languages 14, no. 1 (January 1989): 35–51. http://dx.doi.org/10.1016/0096-0551(89)90029-5.

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44

Chi, Gyoo Yong. "Toward a logical integrative medicine: beyond data accumulation." Integrative Medicine Research 9, no. 4 (December 2020): 100414. http://dx.doi.org/10.1016/j.imr.2020.100414.

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45

Vardi, M. Y. "The universal-relation data model for logical independence." IEEE Software 5, no. 2 (March 1988): 80–85. http://dx.doi.org/10.1109/52.2015.

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46

Yang, Tian Ming, Zhen Tang, Ya Ping Wan, and Wei Sun. "Logical Data Deletion in High-Performance De-Duplication Backup." Applied Mechanics and Materials 321-324 (June 2013): 2519–23. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.2519.

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Data de-duplication divides backup stream into chunks and eliminates duplicate chunks across the entire system, thus remarkably reduces the storage and bandwidth requirement for backups. However, this technique also introduced many new problems among which the performance problem has been resolved by many of the existing solutions, while the logical data deletion problem is not well studied till now. This paper studied the logical data deletion mechanism in de-duplication backup systems, analyzed the memory overhead of the Bloom filter, which supports both high performance de-duplication and logical data deletion, and proposed a lazy deletion method to minimize the influence of logical data deletion on de-duplication performance.
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47

Li, Yan, Zhi Peng Wang, and Hao Xu. "L-R Logical Key Tree Management Scheme Applying Hierarchical Data Processing." Advanced Materials Research 532-533 (June 2012): 753–57. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.753.

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This paper proves multicast management belongs to problems of HDP, provides the cost analytical model of multicast logical key tree which applies HDPT. Based on the above analysis, the author proposes an improved multicast logical key management scheme termed as L-R logical key tree management scheme. L-R logical key tree management scheme is a kind of imbalanced multicast logical key tree, which is based on periodic re-key, according member nodes’ joining and leaving different probability, it employs different multicast key management strategy. The paper provides the design and calculation of the scheme, and realizes it in a simulation. By comparing this scheme with the typical LKH one, it shows clearly the scheme proposed in the paper is of high effect and low cost.
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48

Karmiati, Sri. "PENERAPAN METODE PENEMUAN TERBIMBING DALAM MENINGKATKAN KEMAMPUAN BERPIKIR LOGIS ANAK USIA 3-4 TAHUN DI PPT TERATAI KENJERAN SURABAYA." MOTORIC 4, no. 1 (June 28, 2020): 130–38. http://dx.doi.org/10.31090/m.v4i1.1022.

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Abstract This study aims to determine the increase in the ability to think logically children aged 3-4 years in PPT Kenjeran Surabaya through the application of the Guided Discovery Method. This type of research is classroom action research with subjects of 15 children aged 3-4 years and the object is the application of guided discovery methods in improving logical thinking skills. Data collection using the method of observation, interviews and documentation. The data collected is analyzed and reflected descriptively quantitatively. The results showed that in the first cycle of action as many as 4 children (25%) the ability of logical thinking children develop very well (BSB) this shows that the level of children's logical thinking ability is still low. Actions in cycle II have an increase in the ability to think logically by 13 children (80%) with very well developed criteria (BSB). So it can be concluded that the application of the Guided Discovery Method in improving the logical thinking ability of children aged 3-4 years in PPT Lotus Surabaya can be applied with good results. Key words: Guided Discovery Method, logical thinking
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49

CABIBBO, LUCA, and RICCARDO TORLONE. "AN ARCHITECTURE FOR DATA WAREHOUSING SUPPORTING DATA INDEPENDENCE AND INTEROPERABILITY." International Journal of Cooperative Information Systems 10, no. 03 (September 2001): 377–97. http://dx.doi.org/10.1142/s0218843001000394.

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We report on the design of a novel architecture for data warehousing based on the introduction of an explicit "logical" layer to the traditional data warehousing framework. This layer serves to guarantee a complete independence of OLAP applications from the physical storage structure of the data warehouse and thus allows users and applications to manipulate multidimensional data ignoring implementation details. For example, it makes possible the modification of the data warehouse organization (e.g. MOLAP or ROLAP implementation, star scheme or snowflake scheme structure) without influencing the high level description of multidimensional data and programs that use the data. Also, it supports the integration of multidimensional data stored in heterogeneous OLAP servers. We propose [Formula: see text], a simple data model for multidimensional databases, as the reference for the logical layer. [Formula: see text] provides an abstract formalism to describe the basic concepts that can be found in any OLAP system (fact, dimension, level of aggregation, and measure). We show that [Formula: see text] databases can be implemented in both relational and multidimensional storage systems. We also show that [Formula: see text] can be profitably used in OLAP applications as front-end. We finally describe the design of a practical system that supports the above logical architecture; this system is used to show in practice how the architecture we propose can hide implementation details and provides a support for interoperability between different and possibly heterogeneous data warehouse applications.
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50

Mikheyenkova, M. A. "On the logical tools of intelligent sociological data analysis." Scientific and Technical Information Processing 37, no. 6 (December 2010): 386–97. http://dx.doi.org/10.3103/s0147688210060031.

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