Temporal Databases Research Articles

Interval-based temporal functional dependencies: specification and verification

In the temporal database literature, every fact stored in a database may be equipped with two temporal dimensions: the valid time, which describes the time when the fact is true in the modeled reality, and the transaction time, which describes the time when the fact is current in the database and can be retrieved. Temporal functional dependencies (TFDs) add valid time to classical functional dependencies (FDs) in order to express database integrity constraints over the flow of time. Currently, proposals dealing with TFDs adopt a point-based approach, where tuples hold at specific time points, to express integrity constraints such as "for each month, the salary of an employee depends only on his role". To the best of our knowledge, there are no proposals dealing with interval-based temporal functional dependencies (ITFDs), where the associated valid time is represented by an interval and there is the need of representing both point-based and interval-based data dependencies. In this paper, we propose ITFDs based on Allen's interval relations and discuss their expressive power with respect to other TFDs proposed in the literature: ITFDs allow us to express interval-based data dependencies, which cannot be expressed through the existing point-based TFDs. ITFDs allow one to express constraints such as "employees starting to work the same day with the same role get the same salary" or "employees with a given role working on a project cannot start to work with the same role on another project that will end before the first one". Furthermore, we propose new algorithms based on B-trees to efficiently verify the satisfaction of ITFDs in a temporal database. These algorithms guarantee that, starting from a relation satisfying a set of ITFDs, the updated relation still satisfies the given ITFDs.

On-shelf utility mining with negative item values

On-shelf utility mining has recently received interest in the data mining field due to its practical considerations. On-shelf utility mining considers not only profits and quantities of items in transactions but also their on-shelf time periods in stores. Profit values of items in traditional on-shelf utility mining are considered as being positive. However, in real-world applications, items may be associated with negative profit values. This paper proposes an efficient three-scan mining approach to efficiently find high on-shelf utility itemsets with negative profit values from temporal databases. In particular, an effective itemset generation method is developed to avoid generating a large number of redundant candidates and to effectively reduce the number of data scans in mining. Experimental results for several synthetic and real datasets show that the proposed approach has good performance in pruning effectiveness and execution efficiency.

An Indeterminacy Temporal Data Model Based on Probability

There are many kinds of indeterminacy temporal data in temporal database. Therefore, many researchers have focused on building indeterminacy temporal data models. Unfortunately, established models can’t adequately address the challenges posed by indeterminacy temporal information, and can’t adapt to all sorts of involved applications. In this paper, we propose a temporal data model, named BPTM (Temporal model based on probability), to manage the indeterminacy temporal semantics of indeterminacy data. Firstly, we present our tuple-timestamp method to represent and store these temporal data including determinacy and indeterminacy data. Then we introduce the temporal primitives to process temporal relations needed in BPTM. A new probability method is brought forward to get potential information among these indeterminacy data. At last a query example based on CPR (Computer-based Patient Record) is given to show that our method is effective and feasible. DOI: http://dx.doi.org/10.11591/telkomnika.v11i11.3516

Optimizing frequent time-window selection for association rules mining in a temporal database using a variable neighbourhood search

In this study, we investigate the problem of maximum frequent time-window selection (MFTWS) that appears in the process of discovering association rules time-windows (ARTW). We formulate the problem as a mathematical model using integer programming that is a typical combination problem with a solution space exponentially related to the problem size. A variable neighbourhood search (VNS) algorithm is developed to solve the problem with near-optimal solutions. Computational experiments are performed to test the VNS algorithm against a benchmark problem set. The results show that the VNS algorithm is an effective approach for solving the MTFWS problem, capable of discovering many large-one frequent itemset with time-windows (FITW) with a larger time-coverage rate than the lower bounds, thus laying a good foundation for mining ARTW.

The Effect of HIV on Incidence of Colorectal Cancer (CRC) In African Americans (AA)

Purpose: We seek to determine whether AA infected with HIV are at a higher risk for developing CRC compared to AA not infected, and to determine whether infected individuals should be screened earlier than the current guidelines suggest. Methods: A temporal database analysis of electronic medical records from the EPIC system (using the EXPLORYS database from 1998 to 2013) covering several medical facilities matching inclusion criteria with a diagnosis of HIV and CRC. Patients were divided into four cohorts including: AA with CRC and HIV, AA without HIV and with CRC, Caucasians with CRC and HIV, and Caucasians without HIV and with CRC. Results: Age of diagnosis of CRC was checked in each group with ages ranging from 40 to 79 years. AA with HIV and CRC totaled 3,150 compared to 20,970 without HIV and with CRC. When age groups were compared there was significance found between ages of 50-54 years (15% with HIV vs. 9% without HIV) and 55-59 years (20% with HIV vs. 13% without HIV). Caucasians with HIV and CRC totaling 1,580 were also compared to Caucasians with CRC and without HIV totaling 142,150. When these age groups were compared there was significance also found between ages of 50-54 years (16% with HIV vs. 10% without HIV) and 55-59 years (19% with HIV vs. 13% without HIV). There was no significance found between race when age and percent of CRC were compared for patients with HIV and without HIV. Relative risk (RR) for patients with HIV developing CRC in AA was 6 and in Caucasians the RR was 2.4. Conclusion: This study suggests that HIV has a profound effect in age of diagnosis and incidence of CRC in both AA and Caucasians. There was greater than a 45% increase in CRC found in the age groups of 50-59 years, the current recommended age for screening. There was no significant difference in incidence of CRC between each race, implying that HIV is the main factor. However, the RR suggests some difference in extent of risk. We recommend HIV be considered a risk factor for early CRC screening with screening occurring at age 40 years.FigureFigure

Application of 3 "S" Technologies in Information Extraction of Nuclear Leakage Accident

The paper analyzes the theories of nuclear leakage accident information extraction based on 3S technology, and studying roundly the building process of GIS spatial and temporal databases; the contents of RS images collection and the function of GPS geography position information. In addition, information extraction and analysis methods both on spatial dimension and temporal dimension are summarized. The spatial distribution and trend of radiation leak after the nuclear accident are simulated by trend surface model. Calculation and mapping shows the variation of the radiation value with time using time series analysis model.

Comparisons of three data storage models in parametric temporal databases

The parametric temporal data model captures a real world entity in a single tuple, which reduces query language complexity. Such a data model, however, is difficult to be implemented on top of conventional databases because of its unfixed attribute sizes. XML is a matured technology and can be an elegant solution for such challenge. Representing data in XML trigger a question about storage efficiency. The goal of this work is to provide a straightforward answer to such a question. To this end, we compare three different storage models for the parametric temporal data model and show that XML is not worse than any other approaches. Furthermore, XML outperforms the other storages under certain conditions. Therefore, our simulation results provide a positive indication that the myth about XML is not true in the parametric temporal data model.

POSSIBILISTIC EVALUATION OF SETS

In the past decades, the theory of possibility has been developed as a theory of uncertainty that is compatible with the theory of probability. Whereas probability theory tries to quantify uncertainty that is caused by variability (or equivalently randomness), possibility theory tries to quantify uncertainty that is caused by incomplete information. A specific case of incomplete information is that of ill-known sets, which is of particular interest in the study of temporal databases. However, the construction of possibility distributions in the case of ill-known sets is known to be overly complex. This paper contributes to the study of ill-known sets by investigating the inference of uncertainty when constraints are specified over ill-known values. More specific, in this paper it is investigated how the knowledge about constraint satisfaction can be inferred if the constraints themselves are defined by means of ill-known values. It is shown how such reasoning can contribute to the study of (fuzzy) temporal databases.

Parametric database approach integration for handling temporal data in GIS

In large spatial, temporal, and spatio-temporal databases, the concept of dimensions – time and space – is central and their efficient processing has always been a great concern. A Geographic Information System (GIS) is widely known for its efficient and sophisticated handling of the spatial databases; however, integration of the time domain introduces additional complexities. Considerable research is ongoing in this area. Though numerous approaches have already been proposed in the literature to address the time-related challenges in GIS community, such as the development of temporal GIS data models, this study contributes to these ongoing efforts through extensions to the Parametric Data Model. Parametric database approaches (Parametric Data Model) have been developed to handle dimensional data such as space, time, belief, etc. In this study, the time dimension is of specific interest and we propose the integration of parametric approaches to GIS to absorb the temporal burden. The temporal processing of the data is done using parametric approaches that address the limitation of GIS to support temporal queries and temporal analysis of phenomena. Using the AutoConViz tool, developed for spatio-temporal data modeling and visualization, results can be directly integrated into standard GIS software programs such as ArcGIS and further processed seamlessly along with and in a similar manner to other attributes.

Querying now-relative data

Now-relative temporal data play an important role in most temporal applications, and their management has been proved to impact in a crucial way the efficiency of temporal databases. Though several temporal relational approaches have been developed to deal with now-relative data, none of them has provided a whole temporal algebra to query them. In this paper we overcome such a limitation, by proposing a general algebra which is parametrically adapted to cope with the relational approaches to now-relative data in the literature, i.e., MIN, MAX, NULL and POINT approaches. Besides being general enough to provide a query language for several approaches in the literature, our algebra has been designed in such a way to satisfy several theoretical and practical desiderata: closure with respect to representation languages, correctness with respect to the “consensus” BCDM semantics, reducibility to the standard non-temporal algebra (which involves interoperability with non-temporal relational databases), implementability and efficiency. Indeed, the experimental evaluation we have drawn on our implementation has shown that only a slight overhead is added by our treatment of now-relative data (with respect to an approach in which such data are not present).

Coping with Events in Temporal Relational Databases

Event relations are used in many temporal relational database approaches to represent facts occurring at time instants. However, to the best of our knowledge, none of such approaches fully copes with the definition of events as provided, e.g., by the “consensus” temporal database glossary. We propose a new approach which overcomes such a limitation, allowing one to cope with multiple events occurring in the same temporal granule. This move involves major extensions to current approaches, since indeterminacy about the time and number of occurrences of events need to be faced. Specifically, we have introduced a new data model, and new definitions of relational algebraic operators coping with the above issues, and we have studied their reducibility. Last, but not least, we have shown that our approach can be easily extended in order to cope with a general form of temporal indeterminacy. Such an extension further increases the applicability of our approach.

An intensional approach for periodic data in relational databases

Periodic data play a major role in many application domains, spanning from manufacturing to office automation, from scheduling to data broadcasting. In many of such domains, the huge number of repetitions make the goal of extesionally storing and accessing such data very challenging. In this paper, we propose a new methodology, based on an intensional representation of periodic data. The representation model we propose captures the notion of periodic granularity provided by the temporal database glossary, and is an extension of the TSQL2 temporal relational data model. We define the algebraic operators, and introduce access algorithms to cope with them, proving that they are correct with respect to the traditional extesional approach. We also provide an experimental evaluation of our approach.

Extending BCDM to Cope with Proposals and Evaluations of Updates

The cooperative construction of data/knowledge bases has recently had a significant impulse (see, e.g., Wikipedia [1]). In cases in which data/knowledge quality and reliability are crucial, proposals of update/insertion/deletion need to be evaluated by experts. To the best of our knowledge, no theoretical framework has been devised to model the semantics of update proposal/ evaluation in the relational context. Since time is an intrinsic part of most domains (as well as of the proposal/evaluation process itself), semantic approaches to temporal relational databases (specifically, Bitemporal Conceptual Data Model (henceforth, BCDM) [2]) are the starting point of our approach. In this paper, we propose BCDMPV, a semantic temporal relational model that extends BCDM to deal with multiple update/insertion/deletion proposals and with acceptances/rejections of proposals themselves. We propose a theoretical framework, defining the new data structures, manipulation operations and temporal relational algebra and proving some basic properties, namely that BCDMPV is a consistent extension of BCDM and that it is reducible to BCDM. These properties ensure consistency with most relational temporal database frameworks, facilitating implementations.

A temporal database forecasting algebra

Though forecasting methods are used in numerous fields, we have seen no work on providing a general theoretical framework to build forecast operators into temporal databases, producing an algebra that extends the relational algebra. In this paper, we first develop a formal definition of a forecast operator as a function that satisfies a suite of forecast axioms. Based on this definition, we propose three families of forecast operators called deterministic, probabilistic, and possible worlds forecast operators. Additional properties of coherence, orthogonality, monotonicity, and fact preservation are identified that these operators may satisfy (but are not required to). We show how deterministic forecast operators can always be encoded as probabilistic forecast operators, and how both deterministic and probabilistic forecast operators can be expressed as possible worlds forecast operators. Issues related to the complexity of these operators are studied, showing the relative computational tradeoffs of these types of forecast operators. We explore the integration of different forecast operators with standard relational operators in temporal databases—including extensions of the relational algebra for the probabilistic and possible worlds cases—and propose several policies for answering forecast queries. Instances where these different forecast policies are equivalent have been identified, and can form the basis of query optimization in forecasting. These policies are evaluated empirically using a prototype implementation of a forecast query answering system and several forecast operators.

Managing proposals and evaluations of updates to medical knowledge: Theory and applications

The process of keeping up-to-date the medical knowledge stored in relational databases is of paramount importance. Since quality and reliability of medical knowledge are essential, in many cases physicians’ proposals of updates must undergo experts’ evaluation before possibly becoming effective. However, until now no theoretical framework has been provided in order to cope with this phenomenon in a principled and non-ad hoc way. Indeed, such a framework is important not only in the medical domain, but in all Wikipedia-like contexts in which evaluation of update proposals is required. In this paper we propose GPVM (General Proposal Vetting Model), a general model to cope with update proposal⧹evaluation in relational databases. GPVM extends the current theory of temporal relational databases and, in particular, BCDM – Bitemporal Conceptual Data Model – “consensus” model, providing a new data model, new operations to propose and accept⧹reject updates, and new algebraic operators to query proposals. The properties of GPVM are also studied. In particular, GPVM is a consistent extension of BCDM and it is reducible to it.These properties ensure consistency with most relational temporal database frameworks, facilitating implementation on top of current frameworks and interoperability with previous approaches.

Introducing an annotated bibliography on temporal and evolution aspects in the semantic web

Time is a pervasive dimension of reality as everything evolves as time elapses. Therefore, Web-based information systems and knowledge representation tools at least mirror, and often have to capture, the time-varying and evolutionary nature of the phenomena they model and of the activities they support. This aspect has been acknowledged and long studied in the field of temporal databases [Jensen and Snodgrass 2009] but it truly applies also to the World Wide Web and Semantic Web in particular. Several papers addressing, in an explicit or implicit way, the representation and management of time and evolution in the Semantic Web appeared recently and, on some aspects, showed a clear upward trend in last years, witnessing a sustained and/or growing research interest. Reflecting and acknowledging such interest, we started in 2011 to collect references concerning the handling of time and evolution issues in Semantic Web research. As it was for [Grandi 2003], the purpose of this collection was to compile a bibliography which could be of help, in particular, to students and young researchers. As a result of such almost endless work, we wrote an annotated bibliography [Grandi 2012], whose latest version is available on the Web at URL:

Mining Approach for Updating Sequential Patterns

We are given a large database of customer transactions, where each transaction consists of customer- id, transaction time, and the items bought in the transaction. The discovery of frequent sequences in temporal databases is an important data mining problem. Most current work assumes that the database is static, and a database update requires rediscovering all the patterns by scanning the entire old and new database. We consider the problem of the incremental mining of sequential patterns when new transactions or new customers are added to an original database. In this paper, we propose novel techniques for maintaining sequences in the presence of a) database updates, and b) user interaction (e.g. modifying mining parameters). This is a very challenging task, since such updates can invalidate existing sequences or introduce new ones.

Effects of Sea Level Variation on Biological and Chemical Concentrations in a Coastal Upwelling Ecosystem

Oscillations in sea level due to meteorological forces related to wind and pressure affect the regular tides and modify the sea level conditions, mainly in restricted waters such as bays. Investigations surrounding these variations and the biological and chemical response are important for monitoring coastal regions mainly where upwelling shelf systems occur. A spatial and temporal database from Quick Scatterometer satellite vector wind, surface stations from the Southeast coast of Brazil and surface seawater data collected in Anjos Bay, Arraial do Cabo city, northeast of Rio de Janeiro State were used to investigate the meteorological influences in the variability of the dissolved oxygen, nutrients, meroplankton larvae and chlorophyll-a concentrations. Multivariate statistical approaches such as Principal Component Analysis (PCA) and Clustering Analysis (CA) were applied to verify spatial and temporal variances. A correlation matrix was also verified for different water masses in order to identify the relationship between the above parameters. A seasonal variability of the meteorological residual presents a well-defined pattern with maximum peaks in autumn/winter and minimum during spring/summer with negative values, period of occurrence of upwelling in this region. This lowering of the sea level is in accordance with the increasing of nutrients and meroplankton larvae for the same period. CA showed six groups and an importance of the zonal and meridional wind variability, including these variables in a single cluster. PCA retained eight components, explaining 64.10% of the total variance of data set. Some clusters and loadings have the same variables, showing the importance of the sea-air interaction.

Extraction of a temporal conceptual model from a relational database

Related to the problem of temporal database design, a relational target model has been designed from a temporal conceptual model. Extraction of the temporal conceptual model from a relational model is called the reverse engineering of the relational model. Solving this problem will facilitate an upgraded temporal information system. In this research, we construct a conceptual model which has been used to design the temporal relational model. This approach of extraction is based on characteristics of the set of attributes, the primary key, and the set of foreign keys of the relational schema in the temporal relational model. Thereby, we propose conversion rules relating to the identification of the components existing within the temporal conceptual model accordingly.

A Diy Approach to Uni-Temporal Database Implementation

when historical versions of data are concerned for a MIS (Management Information System) we naturally might resort to temporal database products. These bi-temporal products, however, are often extravagant and not easily mastered to most of MIS practitioners. Hence we present a plain DIY (do it yourself) solution, the Audit & Change Logs Mechanism-based approach--ACLM, to meet the uni-temporal requirement from restoring historical versions of data. With ACLM programmers can code SQL scripts on demand to trace and replay any snapshot of historical data version via RDBMS built-in functions, they need not to shift away from their usual way of coding stored procedures for data maintenance. Besides, the ACLM approach is compatible with mega-data change, and its additive overhead was instantiated imperceptible for throughputs of routine access with a typical scenario.