Journal articles: 'Temporal database'

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Böhlen, Michael H. "Temporal database system implementations." ACM SIGMOD Record 24, no. 4 (December 1995): 53–60. http://dx.doi.org/10.1145/219713.219758.

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ANDROUTSOPOULOS, ION, GRAEME RITCHIE, and PETER THANISCH. "Time, tense and aspect in natural language database interfaces." Natural Language Engineering 4, no. 3 (September 1998): 229–76. http://dx.doi.org/10.1017/s1351324998001971.

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Most existing Natural Language Database Interfaces (NLDB) were designed to be used with database systems that provide very limited facilities for manipulating time-dependent data, and they do not support adequately temporal linguistic mechanisms (verb tenses, temporal adverbials, temporal subordinate clauses, etc.). The database community is becoming increasingly interested in temporal database systems, which are intended to store and manipulate in a principled manner information not only about the present, but also about the past and future. When interfacing to temporal databases, supporting temporal linguistic mechanisms becomes crucial.We present a framework for constructing Natural Language Interfaces for Temporal Databases (NLTDB), which draws on research in tense and aspect theories, temporal logics and temporal databases. The framework consists of a temporal intermediate representation language, called TOP, an HPSG grammar that maps a wide range of questions involving temporal mechanisms to appropriate TOP expressions, and a provably correct method for translating from TOP to TSQL2, TSQL2 being a recently proposed temporal extension of the SQL database language. This framework was employed to implement a prototype NLTDB.

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Koszela, Jarosław, and Paulina Szczepańczyk-Wysocka. "Concept and assumptions about the temporal graph database." MATEC Web of Conferences 210 (2018): 04017. http://dx.doi.org/10.1051/matecconf/201821004017.

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The article outlines existing solutions in the area of graphs and temporal databases. It provides explanation for why the temporal graph database was created. Furthermore, the article also describes the concept and assumptions about the temporal graph database, including a proposal of two methods for representing temporal data in graph databases. Full write method assumes creating a new database object for each state of being. While incremental method writes only such features and relationships that were subject to change. Regardless of the data write method used, the data may be returned in a historically unordered or ordered manner. The article outlines assumptions for both methods of representing data.

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Kotikov, P. E., A. A. Nechay, and V. A. Zatsepin. "Temporal database and query language." Science and Modernity, no. 2 (2014): 60–68. http://dx.doi.org/10.17117/ns.2014.02.060.

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Parisi, Francesco, Amy Sliva, and V. S. Subrahmanian. "A temporal database forecasting algebra." International Journal of Approximate Reasoning 54, no. 7 (September 2013): 827–60. http://dx.doi.org/10.1016/j.ijar.2013.01.010.

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Kung, C. H. "On verification of database temporal constraints." ACM SIGMOD Record 14, no. 4 (May 1985): 169–79. http://dx.doi.org/10.1145/971699.318911.

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Theodoulidis, Babis, Aziz Ait-Braham, George Andrianopoulos, Jayant Chaudhary, George Karvelis, and Simon Sou. "The ORES temporal database management system." ACM SIGMOD Record 23, no. 2 (June 1994): 511. http://dx.doi.org/10.1145/191843.191967.

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Jensen, C. S., J. Clifford, S. K. Gadia, A. Segev, and Richard Thomas Snodgrass. "A glossary of temporal database concepts." ACM SIGMOD Record 21, no. 3 (September 1992): 35–43. http://dx.doi.org/10.1145/140979.140996.

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Motakis, Iakovos, and Carlo Zaniolo. "Temporal aggregation in active database rules." ACM SIGMOD Record 26, no. 2 (June 1997): 440–51. http://dx.doi.org/10.1145/253262.253359.

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Kahn, M. G., L. M. Fagan, and S. Tu. "Extensions to the Time-Oriented Database Model to Support Temporal Reasoning in Medical Expert Systems." Methods of Information in Medicine 30, no. 01 (1991): 04–14. http://dx.doi.org/10.1055/s-0038-1634816.

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Physicians faced with diagnostic and therapeutic decisions must reason about clinical features that change over time. Database-management systems (DBMS) can increase access to patient data, but most systems are limited in their ability to store and retrieve complex temporal information. The Time-Oriented Databank (TOD) model, the most widely used data model for medical database systems, associates a single time stamp with each observation. The proper analysis of most clinical data requires accounting for multiple concurrent clinical events that may alter the interpretation of the raw data. Most medical DBMSs cannot retrieve patient data indexed by multiple clinical events. We describe two logical extensions to TOD-based databases that solve a set of temporal reasoning problems we encountered in constructing medical expert systems. A key feature of both extensions is that stored data are partitioned into groupings, such as sequential clinical visits, clinical exacerbations, or other abstract events that have clinical decision-making relevance. The temporal network (TNET) is an object-oriented database that extends the temporal reasoning capabilities of ONCOCIN, a medical expert system that provides chemotherapy advice. TNET uses persistent objects to associate observations with intervals of time during which “an event of clinical interest” occurred. A second object-oriented system, called the extended temporal network (ETNET), is both an extension and a simplification of TNET. Like TNET, ETNET uses persistent objects to represent relevant intervals; unlike the first system, however, ETNET contains reasoning methods (rules) that can be executed when an event “begins”, and that are withdrawn when that event “concludes”. TNET and ETNET capture temporal relationships among recorded information that are not represented in TOD-based databases. Although they do not solve all temporal reasoning problems found in medical decision making, these new structures enable patient database systems to encode complex temporal relationships, to store and retrieve patient data based on multiple clinical contexts and, in ETNET, to modify the reasoning methods available to an expert system based on the onset or conclusion of specific clinical events.

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Ravikumar, Penugonda, Palla Likhitha, Bathala Venus Vikranth Raj, Rage Uday Kiran, Yutaka Watanobe, and Koji Zettsu. "Efficient Discovery of Periodic-Frequent Patterns in Columnar Temporal Databases." Electronics 10, no. 12 (June 19, 2021): 1478. http://dx.doi.org/10.3390/electronics10121478.

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Discovering periodic-frequent patterns in temporal databases is a challenging problem of great importance in many real-world applications. Though several algorithms were described in the literature to tackle the problem of periodic-frequent pattern mining, most of these algorithms use the traditional horizontal (or row) database layout, that is, either they need to scan the database several times or do not allow asynchronous computation of periodic-frequent patterns. As a result, this kind of database layout makes the algorithms for discovering periodic-frequent patterns both time and memory inefficient. One cannot ignore the importance of mining the data stored in a vertical (or columnar) database layout. It is because real-world big data is widely stored in columnar database layout. With this motivation, this paper proposes an efficient algorithm, Periodic Frequent-Equivalence CLass Transformation (PF-ECLAT), to find periodic-frequent patterns in a columnar temporal database. Experimental results on sparse and dense real-world and synthetic databases demonstrate that PF-ECLAT is memory and runtime efficient and highly scalable. Finally, we demonstrate the usefulness of PF-ECLAT with two case studies. In the first case study, we have employed our algorithm to identify the geographical areas in which people were periodically exposed to harmful levels of air pollution in Japan. In the second case study, we have utilized our algorithm to discover the set of road segments in which congestion was regularly observed in a transportation network.

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Pandey, Anjana, and K. R. Pardasani. "PPCI Algorithm for Mining Temporal Association Rules in Large Databases." Journal of Information & Knowledge Management 08, no. 04 (December 2009): 345–52. http://dx.doi.org/10.1142/s0219649209002440.

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In this paper an attempt has been made to develop a progressive partitioning and counting inference approach for mining association rules in temporal databases. A temporal database like a sales database is a set of transactions where each transaction T is a set of items in which each item contains an individual exhibition period. The existing models of association rule mining have problems in handling transactions due to a lack of consideration of the exhibition period of each individual item and lack of an equitable support counting basis for each item. As a remedy to this problem we propose an innovative algorithm PPCI that combines progressive partition approach with counting inference method to discover association rules in a temporal database. The basic idea of PPCI is to first segment the database into sub-databases in such a way that items in each sub-database will have either a common starting time or a common ending time. Then for each sub-database, PPCI progressively filters 1-itemset with a cumulative filtering threshold based on vital partitioning characteristics. Algorithm PPCI is also designed to employ a filtering threshold in each partition to prune out those cumulatively infrequent 1-itemsets early and it also uses counting inference approach to minimise as much as possible the number of pattern support counts performed when extracting frequent patterns. Explicitly the execution time of PPCI in order of magnitude is smaller than those required by the schemes which are directly extended from existing methods.

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D'Souza, J., and V. Ng. "Knowledge-rich temporal relation identification and classification in clinical notes." Database 2014 (November 19, 2014): bau109. http://dx.doi.org/10.1093/database/bau109.

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LIU, Dong-Ning, Yong TANG, Shao-Hua TENG, and Zhe LIN. "A Minimal Substructural Logic in Temporal Database." Chinese Journal of Computers 36, no. 8 (March 18, 2014): 1592–601. http://dx.doi.org/10.3724/sp.j.1016.2013.01592.

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15

Petković, Dušan. "Support of Temporal Data in Database Systems." International Journal of Computer Applications 152, no. 4 (October 17, 2016): 26–33. http://dx.doi.org/10.5120/ijca2016911786.

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Haraty, Ramzi A., and Natalie Bekaii. "Towards a Temporal Multilevel Secure Database (TMSDB)." Journal of Computer Science 2, no. 1 (January 1, 2006): 19–28. http://dx.doi.org/10.3844/jcssp.2006.19.28.

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Kline, Nick. "An update of the temporal database bibliography." ACM SIGMOD Record 22, no. 4 (December 1993): 66–80. http://dx.doi.org/10.1145/166635.166659.

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PELEKIS, NIKOS, BABIS THEODOULIDIS, IOANNIS KOPANAKIS, and YANNIS THEODORIDIS. "Literature review of spatio-temporal database models." Knowledge Engineering Review 19, no. 3 (September 2004): 235–74. http://dx.doi.org/10.1017/s026988890400013x.

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Recent efforts in spatial and temporal data models and database systems have attempted to achieve an appropriate kind of interaction between the two areas. This paper reviews the different types of spatio-temporal data models that have been proposed in the literature as well as new theories and concepts that have emerged. It provides an overview of previous achievements within the domain and critically evaluates the various approaches through the use of a case study and the construction of a comparison framework. This comparative review is followed by a comprehensive description of the new lines of research that emanate from the latest efforts inside the spatio-temporal research community.

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Acharya, Pankti P., Deeksha Sarma, and Brian McKinnon. "Trends of temporal bone cancer: SEER database." American Journal of Otolaryngology 41, no. 1 (January 2020): 102297. http://dx.doi.org/10.1016/j.amjoto.2019.102297.

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Kumar, Sushil, Sarita S. Bhadauria, and Roopam Gupta. "A Temporal Database Compression with Differential Method." International Journal of Computer Applications 48, no. 6 (June 30, 2012): 65–68. http://dx.doi.org/10.5120/7356-0273.

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Dyreson, Curtis, Fabio Grandi, Wolfgang Käfer, Nick Kline, Nikos Lorentzos, Yannis Mitsopoulos, Angelo Montanari, et al. "A consensus glossary of temporal database concepts." ACM SIGMOD Record 23, no. 1 (March 1994): 52–64. http://dx.doi.org/10.1145/181550.181560.

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GAGNÉ, JEAN-RAYMOND, and JOHN PLAICE. "A Non-standard Temporal Deductive Database System." Journal of Symbolic Computation 22, no. 5-6 (November 1996): 649–64. http://dx.doi.org/10.1006/jsco.1996.0070.

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Halawani. "Memory Storage Issues of Temporal Database Applications on Relational Database Management Systems." Journal of Computer Science 6, no. 3 (March 1, 2010): 296–304. http://dx.doi.org/10.3844/jcssp.2010.296.304.

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Jaziri, Wassim, Najla Sassi, and Dhouha Damak. "Using Temporal Versioning and Integrity Constraints for Updating Geographic Databases and Maintaining Their Consistency." Journal of Database Management 26, no. 1 (January 2015): 30–59. http://dx.doi.org/10.4018/jdm.2015010102.

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The use of geographic data has become a widespread concern, mainly within applications related to spatial planning and spatial decision-making. Therefore, changing environments require databases adaptable to changes that occur over time. Thus, supporting geographic information evolution is essential and extremely important within changing environments. The evolution is expressed in the geographic database by series of update operations that should maintain its consistency. This paper proposes an approach for updating geographic databases, based on update operators and algorithms of constraints integrity checking. Temporal versioning is used to keep the track of changes. Every version presents the state of the geographic database at a given time. Algorithms of constraints integrity checking allow maintaining the database consistency upon its update. To implement our approach and assist users in the evolution process, the GeoVersioning tool is developed and tested on a sample geographic database.

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Noh, Seo-Young, and Shashi K. Gadia. "Benchmarking temporal database models with interval-based and temporal element-based timestamping." Journal of Systems and Software 81, no. 11 (November 2008): 1931–43. http://dx.doi.org/10.1016/j.jss.2008.01.015.

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Kiran, Rage Uday, Pamalla Veena, Penugonda Ravikumar, Chennupati Saideep, Koji Zettsu, Haichuan Shang, Masashi Toyoda, Masaru Kitsuregawa, and P. Krishna Reddy. "Efficient Discovery of Partial Periodic Patterns in Large Temporal Databases." Electronics 11, no. 10 (May 10, 2022): 1523. http://dx.doi.org/10.3390/electronics11101523.

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Periodic pattern mining is an emerging technique for knowledge discovery. Most previous approaches have aimed to find only those patterns that exhibit full (or perfect) periodic behavior in databases. Consequently, the existing approaches miss interesting patterns that exhibit partial periodic behavior in a database. With this motivation, this paper proposes a novel model for finding partial periodic patterns that may exist in temporal databases. An efficient pattern-growth algorithm, called Partial Periodic Pattern-growth (3P-growth), is also presented, which can effectively find all desired patterns within a database. Substantial experiments on both real-world and synthetic databases showed that our algorithm is not only efficient in terms of memory and runtime, but is also highly scalable. Finally, the effectiveness of our patterns is demonstrated using two case studies. In the first case study, our model was employed to identify the highly polluted areas in Japan. In the second case study, our model was employed to identify the road segments on which people regularly face traffic congestion.

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RANDALL, D. J., H. J. HAMILTON, and R. J. HILDERMAN. "TEMPORAL GENERALIZATION WITH DOMAIN GENERALIZATION GRAPHS." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 02 (March 1999): 195–217. http://dx.doi.org/10.1142/s0218001499000124.

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This paper addresses the problem of using domain generalization graphs to generalize temporal data extracted from relational databases. A domain generalization graph associated with an attribute defines a partial order which represents a set of generalization relations for the attribute. We propose formal specifications for domain generalization graphs associated with calendar (date and time) attributes. These graphs are reusable (i.e. can be used to generalize any calendar attributes), adaptable (i.e. can be extended or restricted as appropriate for particular applications), and transportable (i.e. can be used with any database containing a calendar attribute).

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Song, W., and F. Zhang. "Spatio-temporal topological relationships between land parcels in cadastral database." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-6 (April 23, 2014): 89–92. http://dx.doi.org/10.5194/isprsarchives-xl-6-89-2014.

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There are complex spatio-temporal relationships among cadastral entities. Cadastral spatio-temporal data model should not only describe the data structure of cadastral objects, but also express cadastral spatio-temporal relationships between cadastral objects. In the past, many experts and scholars have proposed a variety of cadastral spatio-temporal data models, but few of them concentrated on the representation of spatiotemporal relationships and few of them make systematic studies on spatiotemporal relationships between cadastral objects. The studies on spatio-temporal topological relationships are not abundant. In the paper, we initially review current approaches to the studies of spatio-temporal topological relationships, and argue that spatio-temporal topological relation is the combination of temporal topology on the time dimension and spatial topology on the spatial dimension. Subsequently, we discuss and develop an integrated representation of spatio-temporal topological relationships within a 3-dimensional temporal space. In the end, based on the semantics of spatiotemporal changes between land parcels, we conclude the possible spatio-temporal topological relations between land parcels, which provide the theoretical basis for creating, updating and maintaining of land parcels in the cadastral database.

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Gard, Matthew, Derrick Hasterok, and Jacqueline A. Halpin. "Global whole-rock geochemical database compilation." Earth System Science Data 11, no. 4 (October 17, 2019): 1553–66. http://dx.doi.org/10.5194/essd-11-1553-2019.

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Abstract. Collation and dissemination of geochemical data are critical to promote rapid, creative, and accurate research and place new results in an appropriate global context. To this end, we have compiled a global whole-rock geochemical database, sourced from various existing databases and supplemented with an extensive list of individual publications. Currently the database stands at 1 022 092 samples with varying amounts of associated sample data, including major and trace element concentrations, isotopic ratios, and location information. Spatial and temporal distribution is heterogeneous; however, temporal distributions are enhanced over some previous database compilations, particularly in ages older than ∼ 1000 Ma. Also included are a range of geochemical indices, various naming schema, and physical property estimates computed on a major element normalized version of the geochemical data for quick reference. This compilation will be useful for geochemical studies requiring extensive data sets, in particular those wishing to investigate secular temporal trends. The addition of physical properties, estimated from sample chemistry, represents a unique contribution to otherwise similar geochemical databases. The data are published in .csv format for the purposes of simple distribution, but exist in a structure format acceptable for database management systems (e.g. SQL). One can either manipulate these data using conventional analysis tools such as MATLAB®, Microsoft® Excel, or R, or upload them to a relational database management system for easy querying and management of the data as unique keys already exist. The data set will continue to grow and be improved, and we encourage readers to contact us or other database compilations within about any data that are yet to be included. The data files described in this paper are available at https://doi.org/10.5281/zenodo.2592822 (Gard et al., 2019a).

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Stantic, Bela, Rodney Topor, Justin Terry, and Abdul Sattar. "Advanced indexing technique for temporal data." Computer Science and Information Systems 7, no. 4 (2010): 679–703. http://dx.doi.org/10.2298/csis101020035s.

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The need for efficient access and management of time dependent data in modern database applications is well recognized and researched. Existing access methods are mostly derived from the family of spatial R-tree indexing techniques. These techniques are particularly not suitable to handle data involving open ended intervals, which are common in temporal databases. This is due to overlapping between nodes and huge dead space found in the database. In this study, we describe a detailed investigation of a new approach called ?Triangular Decomposition Tree? (TD-Tree). The underlying idea for the TD-Tree is to manage temporal intervals by virtual index structures relying on geometric interpretations of intervals, and a space partition method that results in an unbalanced binary tree. We demonstrate that the unbalanced binary tree can be efficiently manipulated using a virtual index. We also show that the single query algorithm can be applied uniformly to different query types without the need of dedicated query transformations. In addition to the advantages related to the usage of a single query algorithm for different query types and better space complexity, the empirical performance of the TD-tree has been found to be superior to its best known competitors.

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Zhang, Weihua, Yi Zhang, Chaobang Gao, and Jiliu Zhou. "Action Recognition by Joint Spatial-Temporal Motion Feature." Journal of Applied Mathematics 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/605469.

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This paper introduces a method for human action recognition based on optical flow motion features extraction. Automatic spatial and temporal alignments are combined together in order to encourage the temporal consistence on each action by an enhanced dynamic time warping (DTW) algorithm. At the same time, a fast method based on coarse-to-fine DTW constraint to improve computational performance without reducing accuracy is induced. The main contributions of this study include (1) a joint spatial-temporal multiresolution optical flow computation method which can keep encoding more informative motion information than recent proposed methods, (2) an enhanced DTW method to improve temporal consistence of motion in action recognition, and (3) coarse-to-fine DTW constraint on motion features pyramids to speed up recognition performance. Using this method, high recognition accuracy is achieved on different action databases like Weizmann database and KTH database.

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Mehmood, Nadeem, Syed Muhmmad Aqil Burney, Kashif Rizwan, Asadullah Shah, and Adnan Nadeem. "Building Spatio-Temporal Database Model Based on Ontological Approach using Relational Database Environment." Mehran University Research Journal of Engineering and Technology 36, no. 4 (October 1, 2017): 891–900. http://dx.doi.org/10.22581/muet1982.1704.13.

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PISSINOU, NIKI, and KIA MAKKI. "A UNIFIED MODEL AND METHODOLOGY FOR TEMPORAL OBJECT DATABASES." International Journal of Cooperative Information Systems 02, no. 02 (June 1993): 201–23. http://dx.doi.org/10.1142/s0218215793000101.

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This paper identifies, explores and provides an approach to handling temporal information in object databases. It specifically involves the design and development of the Temporal Three Dimensional Information Space model that integrates time with objects. The model is based on a small number of simple temporal object constructs and provides a user with the basic primitives for temporal object definition, manipulation and retrieval. This research presents a step towards defining the concepts and techniques for incorporating time in object databases, and provides concrete experimental framework for demonstration thereof. Target Application environments include management and geographic information systems, database visualization and robotics.

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Norvag, Kjetil. "Issues in Transaction-Time Temporal Object Database Systems." Journal of Database Management 12, no. 4 (October 2001): 40–51. http://dx.doi.org/10.4018/jdm.2001100104.

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Rainu Nandal, Rainu Nandal. "Spatio-Temporal Database and Its Models: A Review." IOSR Journal of Computer Engineering 11, no. 2 (2013): 91–100. http://dx.doi.org/10.9790/0661-11291100.

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Gadia, Shashi K., and Chuen-Sing Yeung. "A generalized model for a relational temporal database." ACM SIGMOD Record 17, no. 3 (June 1988): 251–59. http://dx.doi.org/10.1145/971701.50233.

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Bertino, E., C. Bettini, E. Ferrari, and P. Samarati. "A temporal access control mechanism for database systems." IEEE Transactions on Knowledge and Data Engineering 8, no. 1 (1996): 67–80. http://dx.doi.org/10.1109/69.485637.

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Ahn, Ilsoo, and Richard Snodgrass. "Performance evaluation of a temporal database management system." ACM SIGMOD Record 15, no. 2 (June 15, 1986): 96–107. http://dx.doi.org/10.1145/16856.16864.

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Dylla, Maximilian, Iris Miliaraki, and Martin Theobald. "A temporal-probabilistic database model for information extraction." Proceedings of the VLDB Endowment 6, no. 14 (September 2013): 1810–21. http://dx.doi.org/10.14778/2556549.2556564.

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Nascimento, Mario A., Timos Sellis, and Reynold Cheng. "Special issue on spatial and temporal database management." GeoInformatica 19, no. 2 (March 5, 2015): 297–98. http://dx.doi.org/10.1007/s10707-015-0224-z.

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Claramunt, Christophe, Markus Schneider, and Raymond Chi-Wing Wong. "Special issue on spatial and temporal database management." GeoInformatica 21, no. 4 (August 22, 2017): 667–68. http://dx.doi.org/10.1007/s10707-017-0307-0.

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Gelbard, Roy, and Israel Spiegler. "Living with Database Conflicts: A Temporal Branching Technique." Distributed and Parallel Databases 17, no. 3 (May 2005): 251–65. http://dx.doi.org/10.1007/s10619-005-6831-1.

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Suranjan, D. E., P. A. N. Shuh-Shen, and Andrew B. Whinston. "Natural language query processing in a temporal database." Data & Knowledge Engineering 1, no. 1 (June 1985): 3–15. http://dx.doi.org/10.1016/0169-023x(85)90024-2.

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Finger, Marcelo. "Handling database updates in two-dimensional temporal logic." Journal of Applied Non-Classical Logics 2, no. 2 (January 1992): 201–24. http://dx.doi.org/10.1080/11663081.1992.10510782.

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Zhou, Xiao-guang, Jun Chen, Jie Jiang, Jian-jun Zhu, and Zhi-lin Li. "Event-based incremental updating of spatio-temporal database." Journal of Central South University of Technology 11, no. 2 (June 2004): 192–98. http://dx.doi.org/10.1007/s11771-004-0040-3.

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Theodoulidis, C., P. Loucopoulos, and B. Wangler. "A conceptual modelling formalism for temporal database applications." Information Systems 16, no. 4 (January 1991): 401–16. http://dx.doi.org/10.1016/0306-4379(91)90031-4.

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Koubarakis, Manolis. "Database models for infinite and indefinite temporal information." Information Systems 19, no. 2 (March 1994): 141–73. http://dx.doi.org/10.1016/0306-4379(94)90008-6.

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Pérez Montoya, Luis Miguel, and Francisco Javier Moreno Arboleda. "Comparing two spatio-temporal query languages: SQLST and Güting’s language." Ingeniería e Investigación 28, no. 3 (September 1, 2008): 138–44. http://dx.doi.org/10.15446/ing.investig.v28n3.15133.

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Spatio-temporal databases allow us to represent objects and phenomena from the real world which change position or shape as time elapses. Several query languages have been proposed during the last decade to deal with this type of database. Two of these languages have been compared in this paper: SQLST and Güting’s language. The comparison was based on criteria which have been applied to programming languages; however, they were adapted here to evaluate database query languages. The results led to concluding that both languages high degree of expressiveness may affect other criteria such as readability and simplicity, especially in the case of Güting’s language.

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Zhang, Chengcui. "A Survey of Visual Traffic Surveillance Using Spatio-Temporal Analysis and Mining." International Journal of Multimedia Data Engineering and Management 4, no. 3 (July 2013): 42–60. http://dx.doi.org/10.4018/jmdem.2013070103.

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The focus of this survey is on spatio-temporal data mining and database retrieval for visual traffic surveillance systems. In many traffic surveillance applications, such as incident detection, abnormal events detection, vehicle speed estimation, and traffic volume estimation, the data used for reasoning is really in the form of spatio-temporal data (e.g. vehicle trajectories). How to effectively analyze these spatio-temporal data to automatically find its inherent characteristics for different visual traffic surveillance applications has been of great interest. Examples of spatio-temporal patterns extracted from traffic surveillance videos include, but are not limited to, sudden stops, harsh turns, speeding, and collisions. To meet the different needs of various traffic surveillance applications, several application- or event- specific models have been proposed in the literature. This paper provides a survey of different models and data mining algorithms to cover state of the art in spatio-temporal modelling, spatio-temporal data mining, and spatio-temporal retrieval for traffic surveillance video databases. In addition, the database model issues and challenges for traffic surveillance videos are also discussed in this survey.

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Zhang, Jianhai, Zhiyong Feng, Yong Su, Meng Xing, and Wanli Xue. "Riemannian Spatio-Temporal Features of Locomotion for Individual Recognition." Sensors 19, no. 1 (December 23, 2018): 56. http://dx.doi.org/10.3390/s19010056.

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Individual recognition based on skeletal sequence is a challenging computer vision task with multiple important applications, such as public security, human–computer interaction, and surveillance. However, much of the existing work usually fails to provide any explicit quantitative differences between different individuals. In this paper, we propose a novel 3D spatio-temporal geometric feature representation of locomotion on Riemannian manifold, which explicitly reveals the intrinsic differences between individuals. To this end, we construct mean sequence by aligning related motion sequences on the Riemannian manifold. The differences in respect to this mean sequence are modeled as spatial state descriptors. Subsequently, a temporal hierarchy of covariance are imposed on the state descriptors, making it a higher-order statistical spatio-temporal feature representation, showing unique biometric characteristics for individuals. Finally, we introduce a kernel metric learning method to improve the classification accuracy. We evaluated our method on two public databases: the CMU Mocap database and the UPCV Gait database. Furthermore, we also constructed a new database for evaluating running and analyzing two major influence factors of walking. As a result, the proposed approach achieves promising results in all experiments.

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Journal articles on the topic 'Temporal database'

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