Abstract
Time-oriented domains with large volumes of time-stamped information, such as medicine, security information and finance, require useful, intuitive intelligent tools to process large amounts of time-oriented multiple-subject data from multiple sources. We designed and developed a new architecture, the VISualizatIon of Time-Oriented RecordS (VISITORS) system, which combines intelligent temporal analysis and information visualization techniques. The VISITORS system includes tools for intelligent selection, visualization, exploration, and analysis of raw time-oriented data and of derived (abstracted) concepts for multiple subject records. To derive meaningful interpretations from raw time-oriented data (known as temporal abstractions), we use the knowledge-based temporal-abstraction method. A major task in the VISITORS system is the selection of the appropriate subset of the subject population on which to focus during the analysis. Underlying the VISITORS population-selection module is our ontology-based temporal-aggregation (OBTAIN) expression-specification language which we introduce in this study. The OBTAIN language was implemented by a graphical expression-specification module integrated within the VISITORS system. The module enables construction of three types of expressions supported by the language: Select Subjects, Select Time Intervals, and Get Subjects Data. These expressions retrieve a list of subjects, a list of relevant time intervals, and a list of time-oriented subjects’ data sets, respectively. In particular, the OBTAIN language enables population-specification, through the Select Subjects expression, by using an expressive set of time and value constraints. We describe the syntax and semantics of the OBTAIN language and of the expression-specification module. The OBTAIN expressions constructed by the expression-specification module, are computed by a temporal abstraction mediation framework that we have previously developed. To evaluate the expression-specification module, five clinicians and five medical informaticians defined ten expressions, using the expression-specification module, on a database of more than 1,000 oncology patients. After a brief training session, both user groups were able in a short time (mean = 3.3 ± 0.53 min) to construct ten complex expressions using the expression-specification module, with high accuracy (mean = 95.3 ± 4.5 on a predefined scale of 0 to 100). When grouped by time and value constraint subtypes, five groups of expressions emerged. Only one of the five groups (expressions using time-range constraints), led to a significantly lower accuracy of constructed expressions. The five groups of expressions could be clustered into four homogenous groups, ordered by increasing construction time of the expressions. A system usability scale questionnaire filled by the users demonstrated the expression-specification module to be usable (mean score for the overall group = 68), but the clinicians’ usability assessment (60.0) was significantly lower than that of the medical informaticians (76.1).











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Aigner, W., Miksch, S., Müller, W., Schumann, H., & Tominski, C. (2008). Visual methods for analyzing time-oriented data. IEEE Transactions on Visualization and Computer Graphics, 14(1), 47–60.
Allen, J. F. (1983). Maintaining knowledge about temporal intervals. Communications of the ACM, 26(11), 832–843.
Balkir, N., Ozsoyoglu, G., & Ozsoyoglu, Z. (2002). A graphical query language: VISUAL and its query processing. IEEE Transactions on Knowledge and Data Engineering, 14(5), 955–978.
Boaz, D., & Shahar, Y. (2005). A distributed temporal-abstraction mediation architecture for medical databases. Artificial Intelligence in Medicine, 34(1), 3–24.
Bresciani, P., Nori, M., & Pedot, N. (2000). QueloDB: A knowledge based visual query system. In Proceedings of the 2000 international conference on artificial intelligence IC-AI 2000 (vol. III). Las Vegas: CSREA.
Bresciani, P., & Fontana, P. (2002). A knowledge-based query system for biological databases. In Proceedings of flexible query answering systems, FQAS 2002. LNCS (Vol. 2522, pp. 86–89). Berlin: Springer.
Brooke, J. (1996). SUS: A “quick and dirty” usability scale. In P. W. Jordan, B. Thomas, B. A. Weerdmeester, & A. L. McClelland (Eds.), Usability evaluation in industry. London: Taylor and Francis.
Catarci, T., Santucci, G., & Cardiff, J. (1997). Graphical interaction with heterogeneous databases. Very Large Data Base (VLDB). Journal, 6(2), 97–120.
Catarci, T., Mascio, T., Franconi, E., Santucci, G., & Tessaris, S. (2004). An ontology based visual tool for query formulation support. In Proceedings of european conference on artificial intelligence, ECAI.
Chakravarty, S., & Shahar, Y. (2001). Specification and detection of periodicity in clinical data. Methods of Information in Medicine, 40(5), 410–420. Reprinted in: R. Haux, & C. Kulikowski (Eds.), Yearbook of medical informatics 2003 (pp. 296–306). Stuttgart: F.K. Schattauer and The International Medical Informatics Association.
Chittaro, L. (2001) Information visualization and its application to medicine. Artificial Intelligence in Medicine, 22(2), 81–88.
Chittaro, L., Combi, C., & Trapasso, G. (2002) Visual data mining of clinical databases: An application to the hemodialytic treatment based on 3D interactive bar charts. In Proceedings of visual data mining VDM’2002. Finland: Helsinki.
Chittaro, L., & Combi, C. (2003). Visualizing queries on databases of temporal histories: New metaphors and their evaluation. Data & Knowledge Engineering, 44(2), 239–264.
Chu, W., Chih-Cheng, H., Cardenas, A., & Taira, R. (1998). Knowledge-based image retrieval with spatial and temporal constructs. IEEE Transactions Knowledge and Data Engineering, 10(6), 872–888.
Combi, C., Pinciroli, F., Cavallaro, M., & Cucchi, G. (1995). Querying temporal clinical databases with different time granularities: The GCH-OSQL language. In R. M. Gardner (Ed.), 19. annual symposium on computer applications in medical care (pp. 326–330). Philadelphia: Hanley & Belfus.
Combi, C, & Cucchi, G. (1997). GCH-OSQL: A temporally-oriented object-oriented query language based on a three-valued logic. In Proceedings of the 4th international workshop on temporal representation and reasoning
Combi, C., & Montanari, A. (2001). Data models with multiple temporal dimensions: Completing the picture. In K. R. Dittrich, A. Geppert, & M. C. Norrie (Eds.), Caise. LNCS (Vol. 2068, pp. 187–202). Berlin: Springer.
Combi, C., Cucchi, G., & Pinciroli, F. (1997). Applying object-oriented technologies in modeling and querying temporally-oriented clinical databases dealing with temporal granularity and indeterminacy. IEEE Transactions on Information Technology in Biomedicine, 1(2), 100–127.
Combi, C., Montanari, A., & Pozzi, G. (2007). The t4sql temporal query language. CIKM, 2007. In M. J. Silva, A. H. F. Laender, R. A. Baeza-Yates, D. L. McGuinness, B. Olstad, O. H. Olsen, & A. O. Falcao (Eds.), Proceedings of the sixteenth ACM conference on information and knowledge management, CIKM 2007 (pp. 193–202). Portugal: Lisbon.
Das, A., & Musen, M. (1994). A temporal query system for protocol directed decision support. Methods of Information in Medicine, 33(4), 358–370.
Dionisio, J., & Cardenas, A. (1996). MQuery: A visual query language for multimedia, timeline and simulation data. Journal of Visual Languages and Computing, 7, 377–401.
Falkman, G. (2001) Information visualisation in clinical odontology: Multidimensional analysis and interactive data exploration. Artificial Intelligence in Medicine, 22(2), 133–158.
Friedman, C., & Wyatt, J. (1997). Evaluation methods in medical informatics. New York: Springer.
Hearst, M. (2002). User interfaces and visualization. In Y. R. B. Ribeiro-Neto (Ed.) Modern information retrieval (pp. 257–324. Chapter 10). NY: ACM.
Hibino, S., & Rudensteiner, E. (1996). A visual multimedia query language for temporal analysis of video data. In Multimedia database systems: Design and implementation strategies (pp. 123–159). Dordrecht: Kluwer Academic.
Hochheiser, H., & Shneiderman, B. (2001). Visual specification of queries for finding patterns in time-series data. Proceedings of discovery science, 2001, 441–446.
Hochheiser, H., & Shneiderman, B. (2004). Dynamic query tools for time series data sets: Timebox widgets for interactive exploration. Information Visualization Spring 2004, 3(1), 1–18.
German, E., Leibowitz, A., & Shahar, Y. (2009). An architecture for linking medical decision-support applications to clinical databases and its evaluation. Journal of Biomedical Informatics, 42(2), 203–218.
Martins, S., Shahar, Y., Goren-Bar, D., Galperin, M., Kaizer, H., Basso, L., et al. (2008). Evaluation of an architecture for intelligent query and exploration of time-oriented clinical data. Artificial Intelligence in Medicine, 43(1), 17–34.
Narayanan, A., & Shaman, T. (2002). Iconic SQL: Practical issues in the querying of database through structured iconic expressions. Journal of Visual Languages and Computing, 13(6), 623–647.
Navathe, S., & Ahmed, R. (1993). Temporal extensions to the relational model and SQL. In A. Tansel, J. Clifford, S. Gadia, S. Jajodia, A. Segev, & R. Snodgrass (Eds.), Temporal databases: Theory, design, and implementation (pp. 92–109). Redwood City: Benjamin/Cummings.
Nigrin, D., & Kohane, I. S. (1998). Data mining by clinicians. Proceedings of the AMIA Symposium, 1998, 957–961.
Nigrin, D., & Kohane, I. S. (2000). Temporal expressiveness in querying a time-stamp-based clinical database. Journal of American Medical Informatics Association, 7, 152–163.
Plaisant, C., Milash, B., Rose, A., Widoff, S., Shneiderman, B. (1996). Lifelines: Visualizing personal histories. In Proceedings of the CHI’96 (pp. 221–227). Vancouver: ACM.
Plaisant, C., Mushlin, R., Snyder, A., Li, J., Heller, D., Shneiderman, B. (1998). LifeLines: Using visualization to enhance navigation and analysis of patient records. In American medical informatics association annual fall symposium (pp. 760–780).
Sarda, N. (1993). HSQL: A historical query language. In A. Tansel, J. Clifford, S. Gadia, S. Jajodia, A. Segev, & R. Snodgrass (Eds.), Temporal databases: Theory, design, and implementation (pp. 110–140). Redwood City: Benjamin/Cummings.
Shabtai, A., Klimov, D., Shahar, Y., & Elovici, Y. (2006). An intelligent, interactive tool for exploration and visualization of time-oriented security data. Conference on computer and communications security. In Proceedings of the 3rd international workshop on visualization for computer security (pp. 15–22). Virginia, USA: Alexandria.
Shahar, Y. (1997). A framework for knowledge-based temporal abstraction. Artificial Intelligence, 90(1–2), 79–133.
Shahar, Y., Goren-Bar, D., Boaz, D., & Tahan, G. (2006). Distributed, intelligent, interactive visualization and exploration of time-oriented clinical data and their abstractions. Artificial Intelligence in Medicine, 38(2), 115–135.
Shahar, Y., & Musen, M. (1996). Knowledge-based temporal abstraction in clinical domains. Artificial Intelligence in Medicine, 8(3), 267–298.
Shneiderman, B. (1999) Dynamic queries, starfield displays, and the path to spotfire. In Research summary. Maryland: Human–Computer Interaction Laboratory, University of Maryland.
Silva, S., Catarci, T., & Schiel, U. (2002). Formalizing visual interaction with historical databases. Information Systems, 27(7), 487–521.
Silva, S., Schiel, U., & Catarci, T. (1997). Visual query operators for temporal databases. In Proceedings of international workshop on temporal representation and reasoning TIME’97 (pp. 46–53). CA: Los Alamitos.
Snodgrass, R. (1995). The TSQL2 temporal query language. Hingham: Kluwer Academic.
Soukup, T., & Davidson, I. (2002). Visual data mining: Techniques and tools for data visualization and mining. New York: Wiley.
Spenke, M. (2001). Visualization and interactive analysis of blood parameters with InfoZoom. Artificial Intelligence in Medicine, 22(2), 159–172.
Spokoiny, A., & Shahar, Y. (2007). An active database architecture for knowledge-based incremental abstraction of complex concepts from continuously arriving time-oriented raw data. Journal of Intelligent Information Systems, 28(3), 199–231.
Spokoiny, A., & Shahar, Y. (2008). Incremental application of knowledge to continuously arriving time-oriented data. Journal of Intelligent Information Systems, 31(1), 1–33.
Van Der Pol, R. (2003). Dipe-D: A tool for knowledge-based query formulation in information retrieval. Information Retrieval Journal (Vol. 6, issue 1). Netherlands: Springer.
Wang, T., Plaisant, C., Quinn, A., Stanchak, R., Shneiderman, B., & Murphy, S. (2008). Aligning temporal data by sentinel events: Discovering patterns in electronic health records. In SIGCHI Conference on Human Factors in Computing Systems.
Acknowledgements
This research was supported by Deutsche Telekom Labs at Ben-Gurion University of the Negev and the Israel Ministry of Defense, BGU award no. 89357628-01. We thank all the clinicians and medical informaticians who contributed their time to the evaluation. We thank Ms. Efrat German for her work on the Tempura system, and Mr. Ido Hacham and Mr. Shahar Albia for their work on the Multi-TOQ system.
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Appendices
Appendix A.1: A XML-schema for Select Subjects expression



Appendix A.2: A XML-schema for Select Time Intervals expression

Appendix A.3: A XML-schema for Get Subject Data expression

Appendix B.1: A pseudo-code description of the process of computing the Select Subjects expression by the Multi-TOQ module


Appendix B.2: A pseudo-code description of the process of computing the Select Time Intervals expression by the Multi-TOQ module


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Klimov, D., Shahar, Y. & Taieb-Maimon, M. Intelligent selection and retrieval of multiple time-oriented records. J Intell Inf Syst 35, 261–300 (2010). https://doi.org/10.1007/s10844-009-0100-0
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DOI: https://doi.org/10.1007/s10844-009-0100-0