Temporal Data Model Research Articles

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.

Stochastic seasonality of rainfall in New Zealand

AbstractSeasonality is an important source of variation in many processes and needs to be incorporated into rainfall models. The stochastic seasonal rainfall models for high temporal resolution data [Sansom and Thomson, 2010] and daily data (T. Carey‐Smith et al., A hidden seasonal switching model for multisite daily rainfall, manuscript in preparation 2012) both depend on the specification of one day of the year for each season being in the particular season. So, if the seasonality is to be represented by four seasons then it is necessary to provide four dates on each of which it can be said that, every year, the season is of the first, second, third or fourth type respectively on that day of the year. The model fitting can only proceed once these dates, the mid‐seasons, have been provided.In a region of large seasonal rainfall variation the determination of these mid‐seasons would not be difficult and the model fitting not sensitive to their choice. However, although it is clearly evident in the annual pattern of monthly accumulations, New Zealand's rainfall seasonality is not strong and careful assessment of the mid‐season dates is necessary. They need to be estimated with a precision of days and the 55‐year long rainfall records of daily data from 141 stations spread across New Zealand were analysed on a regional basis. The analysis found regionally coherent dates when the mean daily rain rate changed significantly and that over the years these dates could be modelled as a four component von Mises distribution with characteristics consistent with stochastic seasonality.

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.

Moving Object in Different Environment (MODE) using Spatial-Temporal Database Concept

temporal data models and query languages are a topic of growing interest. A spatial-temporal database is a database that embodies spatial, temporal, and spatial-temporal database concepts and captures simultaneously spatial and temporal aspects of data. A spatial data object occupies a certain region of space, which is characterized by its location and boundary. It deals with geometries that change with time. In this research to focus the moving object in different environment with the algorithmic details of each object movement likewise sky, water fall and hills. The positions of moving objects are continuously changing over time. For modeling these moving objects, consider both continuous and discrete models. The position of moving object can be calculated by time, speed, route, hiding position and reflection angle etc. The speed and route are managed as dynamic attributes, and are the motion information of moving object. The attribute uses in whole concept in dynamic. KeywordsTemporal Database-Moving Object- Hiding Value- Location finding-Query analyzation

Uncertain time-series similarity

In the last years there has been a considerable increase in the availability of continuous sensor measurements in a wide range of application domains, such as Location-Based Services (LBS), medical monitoring systems, manufacturing plants and engineering facilities to ensure efficiency, product quality and safety, hydrologic and geologic observing systems, pollution management, and others. Due to the inherent imprecision of sensor observations, many investigations have recently turned into querying, mining and storing uncertain data. Uncertainty can also be due to data aggregation, privacy-preserving transforms, and error-prone mining algorithms. In this study, we survey the techniques that have been proposed specifically for modeling and processing uncertain time series, an important model for temporal data. We provide an analytical evaluation of the alternatives that have been proposed in the literature, highlighting the advantages and disadvantages of each approach, and further compare these alternatives with two additional techniques that were carefully studied before. We conduct an extensive experimental evaluation with 17 real datasets, and discuss some surprising results, which suggest that a fruitful research direction is to take into account the temporal correlations in the time series. Based on our evaluations, we also provide guidelines useful for the practitioners in the field.

Dynamic Human Activity Recognition Based on Improved FNN Model

In this paper, we propose an automatic system that recognizes dynamic human gestures activity, including Arabic numbers from 0 to 9. We assume the gesture trajectory is almost in a plane that called principal gesture plane, then the Least Squares Method is used to estimate the plane and project the 3 D trajectory model onto the principal. An improved FNN model combined with HMM is proposed for dynamic gesture recognition, which combines ability of HMM model for temporal data modeling with that of fuzzy neural network. The proposed algorithm shows that satisfactory performance and high recognition rate.

A Novel Method for Modeling RFID Data

The applications of Radio Frequency Identification (RFID) and Electronic Product Code (EPC) in supply chain management have vast potential in improving effectiveness and efficiencies in solving supply chain problems. RFID data, however, have their own unique characteristics – including aggregation, location, temporal and history-oriented – which have to be fully considered and integrated into the RFID data model which is constructed for RFID application system. In this paper, we use an expressive temporal data model for RFID data. This data model is based on Entity-Relation (ER) model with minimum extension and highlights the state history and temporal semantics of the RFID system business processes. We also show common RFID data tracking and monitoring types, and propose methods to express such queries based on this data model.

Transforming Conceptual Model into Logical Model for Temporal Data Warehouse Security: A Case Study

Extraction–transformation–loading (ETL) processes are responsible for the extraction of data from several sources, their cleansing, customization and insertion into a data warehouse. Data warehouse often store historical information which is extracted from multiple, heterogeneous, autonomous and distributed data sources, thereby, the survival of the organizations depends on the correct management, security and confidentiality of the information. In this paper, we are using the Model Driven Architecture (MDA) approach to represent logical model requirements for secure Temporal Data Warehouses (TDW). We use the Platform-Independent Model (PIM) which does not include information about specific platforms and technologies. Nowadays, the most crucial issue in MDA is the transformation between a PIM and Platform Specific Models PSM. Thus, OMG defines use the Query/View/Transformation (QVT) language, an approach for expressing these MDA transformations. This paper proposes a set of rules to transform PIM model for secure temporal data warehouse (TDW) to PSM model, we apply the QVT language to the development of a secure data warehouse by means of a case study.

A Model Comparison for Spatiotemporal Data in Ubiquitous Environments: A Case Study

In ubiquitous environments, many applications need to process data with time and space dimensions. Because of this, there is growing attention not only on gathering spatiotemporal data in ubiquitous environments, but also on processing such data in databases. In order to obtain the full benefits from spatiotemporal data, we need a data model that naturally expresses the properties of spatiotemporal data. In this paper, we introduce three spatiotemporal data models extended from temporal data models. The main goal of this paper is to determine which data model is less complex in the spatiotemporal context. To this end, we compare their query languages in the complexity aspect because the complexity of a query language is tightly coupled with its underlying data model. Throughout our investigations, we show that it is important to intertwine space and time dimensions and keep one-to-one correspondence between an object in the real world and a tuple in a database in order to naturally express queries in ubiquitous applications.

Modeling Paradigms for Medical Diagnostic Decision Support: A Survey and Future Directions

Use of computer based decision tools to aid clinical decision making, has been a primary goal of research in biomedical informatics. Research in the last five decades has led to the development of Medical Decision Support (MDS) applications using a variety of modeling techniques, for a diverse range of medical decision problems. This paper surveys literature on modeling techniques for diagnostic decision support, with a focus on decision accuracy. Trends and shortcomings of research in this area are discussed and future directions are provided. The authors suggest that-(i) Improvement in the accuracy of MDS application may be possible by modeling of vague and temporal data, research on inference algorithms, integration of patient information from diverse sources and improvement in gene profiling algorithms; (ii) MDS research would be facilitated by public release of de-identified medical datasets, and development of opensource data-mining tool kits; (iii) Comparative evaluations of different modeling techniques are required to understand characteristics of the techniques, which can guide developers in choice of technique for a particular medical decision problem; and (iv) Evaluations of MDS applications in clinical setting are necessary to foster physicians' utilization of these decision aids.

An Integrated Conceptual Model for Temporal Data Warehouse Security

In the past few years, several conceptual approaches have been proposed for the specification of the main multidimensional (MD) properties of the data warehouse (DW) repository. However, most of them deal with isolated aspects of the DW and do not provide designers with an integrated and standard method for designing the whole DW life cycle (ETL processes, data sources, DW repository and so on).Some approaches are depending on specific platform or neglecting important issues in DW design life cycle. Extraction-transformation-loading (ETL) processes play an important role in data warehouse architecture because they are responsible of integrating data from heterogeneous data sources into the DW repository. This paper proposes a conceptual model to refresh data warehouse by (insert, update, delete) data using ETL processes and considering DW security requirements. Firstly, the proposed ETL model is based on Unified Modeling Language (UML), which allows us to accomplish the conceptual modeling of ETL processes .secondly; this part focuses on how to integrate the proposed ETL model with the DW model. The proposed DW model depends on the Model Driven Architecture (MDA). MDA is a standard framework for software development that addresses the complete life cycle of designing, deploying, integrating, and managing applications by using models in software development. This paper proposes an integrated conceptual model for addressing temporal data warehouse security requirements (CMTDWS).

Past and Future of DL-Lite

We design minimal temporal description logics that are capa- ble of expressing various aspects of temporal conceptual data models and investigate their computational complexity. We show that, depending on the required types of temporal and atemporal constraints, the satisfiability problem for temporal knowledge bases in the resulting logics can be NLOGSPACE-, NP- and PSPACE-complete, as well as undecidable.

Memory Storage Issues of Temporal Database Applications on Relational Database Management Systems

Problem statement: Many existing database applications manage time-varying data. These database applications are referred to as temporal databases or time-oriented database applications that are considered as repositories of time-dependent data. Many proposals have been introduced for developing time-oriented database applications, some of which suggest building support for Temporal Database Management Systems (TDBMS) on top of existing non-temporal DBMSs, while others suggest modifying the models of exiting DBMSs or building TDBMS from scratch. Approach: This study addressed several issues concerning developing a technique that enables database designers to understand the way in which time-varying behavior can be modeled and mapped into tabular form. Results: Conventional DBMSs do not have the capability to record and process time-varying aspects of the real world. With growing sophistication of DBMS applications, the lack of temporal support in conventional DBMS raises serious problems when used to develop temporal database. The technique of understanding how to think about time and represent it in formal systems is the topic of this study. We examined how to implement time-varying application in the SQL structured query language by introducing temporal data concepts that need to be simulated in DBMSs which lack temporal supports. We proposed a temporal data model that combines the features of previous temporal models and that reduces the cost of memory storage. Conclusion: We proposed a technique for implementing temporal database on top of exiting non-temporal DBMS. This technique includes five main areas. These areas are temporal database conceptual design, temporal database logical design, integrity constraints preventions in temporal database, modifying and querying temporal database. We proposed a data model for the temporal database based on the data models which are discussed in literature.

Geospatial information systems for Karst: Morpho-Geological Study in Kinta Valley, Perak, Malaysia

The Kinta Valley karst has gone thourgh process of natura; and human-iduced degradation. The topography and lithology in this area has made it possible for accelerated limestone dissolution and the marble has been quarried for many years. The geospatial study to observe the changes in the jarst for over 23 years has been carried out. A databased of sinkhole distribution was created and the changes of limestone topography were analyzed as well as teh other relatively important features. The intersification of natural denudation rate and human activities has given huge impact on the kars. it can be observed byt spatial temporal data model that also has facilitated the delineation of the changes in the topography.&nbsp

An Approach To Correlate the Temporal Information To Facilitate Specimen Selection in the Breast Cancer Research Project.

Abstract Background: Temporal information management is very important in translational research. In the Clinical Breast Care Project (CBCP), the information on subjects and their specimens may be collected at multiple time points using multiple instruments.All such information is stored in an in-house data warehouse. Currently, 4000+ subjects have been enrolled in the study following HIPAA-compliant IRB-approved protocols with 35,000+ specimens collected. Some of the patient's information is static but other data are time dependent. As a result, selecting samples for experiments is a challenge due to complicated temporal relationships between samples and information collected through various instruments.Methods and Results: In the CBCP, the clinical information, blood, and solid tissues of a subject may be collected at multiple time points, associated with the completion of a Core Questionnaire (CQ) for clinical information, and/or a Pathology Checklist (PC) for pathology and sample information. We have designed and implemented an algorithm to use a set of pre-defined flags to precisely describe each sample related to patient's clinical and pathology information in the temporal domain. Five categories (flags) were created to describe the relationship between the sample date (SD) and the CQ date based on whether SD is within 60 days of the CQ date or there is missing data or not. The relationship between blood samples and pathology information is more complicated. Within 90 days, any of the 15 surgical procedures might be performed on a patient and blood samples might be collected before, at the time of, or between any procedures. For some experiments, it is crucial to select blood samples taken before tumor is impacted or severely impacted. Thus, we defined a dozen categories to describe the relationship between the SD and the procedure date (PD), including when the SD is earlier than any PD, equals to the first PD, or between certain procedures. Using these flags we have characterized the relationships between SDs and CQ dates, and between SDs and PDs for all the samples and all the subjects, and stored all the information into two relational tables. The temporal criteria for sample selection are now represented by the relationships between these flags, and can be implemented through several filtering processes. The described algorithm drastically reduces the time needed for precise sample selection from several days for manual efforts to several hours.Discussion: We are in the process of developing a general data model for temporal information management. The method described here is a transitional solution that fulfills our current needs. As an initial effort some of the thresholds for categorizing different temporal conditions are arbitrary, and we are validating them with experimental results for future improvement. Nonetheless, this algorithm has greatly enhanced the efficiency of our subject and specimen selection for wet bench experiments. The same principle can be applied to the future temporal data model solution, for CBCP and other human disease studies. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 4173.

A conceptual schema-based temporal meta database schemas generation technique for 3D objects

In this paper, a temporal meta database for three-dimensional (3D) objects whose properties and relationships are supported by valid time is introduced. Based on our proposed temporal object-oriented conceptual schema model, a conceptual schema of the temporal meta database can be generated from a 3D graphical data source and other particular application requirements. Based on our proposed temporal object relational data model with attribute timestamping, logical schemas of the temporal meta database can be systematically and automatically generated from the conceptual schema. From the temporal meta database, non-temporal/temporal metadata about temporal 3D objects are available for temporal information system users. Convenient access using database languages such as SQL can be performed. Queries over 3D objects using a temporal object relational SQL are demonstrated.

An active product state tracking architecture in logistics sensor networks

Sensor technologies are being introduced as a means to collect the accurate and online information of products in logistics networks. Products with RFID (Radio Frequency Identification) tags can guarantee timely product location visibility. Also, additional sensors can measure location dependent attributes such as temperature and humidity. Representative product centric approaches such as EPC Network and the Dialog system make it possible to secure the item level product information automatically and fast. However, they leave room for more advanced services, especially for active product state tracking service that monitors the locations and attributes of products in a timely manner and triggers exception handling when the constraints associated with the product states are violated. Using state transition model, temporal data model, and publish/subscribe model, this paper proposes an active product state tracking system architecture which is able to track products even when they are enclosed in a box, a pallet, or a container. A simulation based experiment is provided to evaluate the performance of the proposed system.

Recognition of Complex Dynamic Gesture Based on HMM-FNN Model

复杂动态手势识别是利用视频手势进行人机交互的关键问题.提出一种HMM-FNN模型结构.它整合了隐马尔可夫模型对时序数据的建模能力与模糊神经网络的模糊规则构建与推理能力,并将其应用到复杂动态手势的识别中.复杂动态手势具备两大特点:运动特征的可分解性与定义描述的模糊性.针对这两种特性,复杂手势被分解为手形变化、2D平面运动与Z轴方向运动3个子部分,分别利用HMM进行建模,HMM模型对观察子序列的似然概率被作为FNN的模糊隶属度,通过模糊规则推理,最终得到手势的分类类别.HMM-FNN方法将高维手势特征分解为低;Recognition of complex dynamic gesture is a key issue for visual gesture-based human-computer interaction. In this paper, an HMM-FNN model is proposed for gesture recognition, which combines ability of HMM model for temporal data modeling with that of fuzzy neural network for fuzzy rule modeling and fuzzy inference. Complex dynamic gesture has two important properties: Its motion can be decomposed and usually being defined in a fuzzy way. By HMM-FNN, complex gesture is firstly decomposed into three components: Posture changing, movement in 2D plane and movement in Z-axis direction, each of which is modeled by HMM. The likelihood of each HMM to observation sequence is considered as membership value of FNN, and gesture is classified through fuzzy inference of FNN. In this proposed method, high-dimensional gesture feature is transformed into several low-dimensional features, as a result, computational complexity is reduced. Furthermore, human's experience or prior knowledge can be used to build and optimize model structure. Experimental results show that the proposed method is an effective method for recognition of complex dynamic gesture, and is superior to conventional HMM method.

Functional connectivity of cortex, amygdala, insula – fMRI data analysis using vector autoregression

Owing to recent technological advances with high-Tesla MRI scanners, functional imaging of neural tissues with high resolution of the temporal as well as spatial domains comes within reach. Thus, an increasing demand for tools that allow the modeling and evaluation of temporal data, i.e. data that carry sequential information, will likely result. Time series models based on such data can be computed to study the dynamical connectivity of brain structures. We focused on the method of vector autoregression (VAR) by which the strength of sequential interactions among multiple BOLD responses can be assessed, as acquired by fMRI. The method of time series analysis was applied in data sets from 20 subjects listening to auditory stimuli. These stimuli were of an affective nature (a person sobbing; a person laughing) and control stimuli (backward-sobbing, backward-laughter, silence). Each data set consisted of 207 consecutive MR scans. Models composed of 6 variables (i.e., the following regions of interest: Amygdala left/right; Insula left/right; Auditory cortex left/right) were computed. VAR of these variables resulted in a statistically significant model of the sequential interactions among these variables in the sample. It was found that the auditory cortex was directly influenced by the independent variables (the auditory stimuli). Several further interactions were observed, prominently among these an inhibiting effect of the auditory cortex on the amygdala. In addition to these functional results, the methodological merits and limits of the proposed method are discussed.

ArchIS: an XML-based approach to transaction-time temporal database systems

Effective support for temporal applications by database systems represents an important technical objective that is difficult to achieve since it requires an integrated solution for several problems, including (i) expressive temporal representations and data models, (ii) powerful languages for temporal queries and snapshot queries, (iii) indexing, clustering and query optimization techniques for managing temporal information efficiently, and (iv) architectures that bring together the different pieces of enabling technology into a robust system. In this paper, we present the ArchIS system that achieves these objectives by supporting a temporally grouped data model on top of RDBMS. ArchIS' architecture uses (a) XML to support temporally grouped (virtual) representations of the database history, (b) XQuery to express powerful temporal queries on such views, (c) temporal clustering and indexing techniques for managing the actual historical data in a relational database, and (d) SQL/XML for executing the queries on the XML views as equivalent queries on the relational database. The performance studies presented in the paper show that ArchIS is quite effective at storing and retrieving under complex query conditions the transaction-time history of relational databases, and can also assure excellent storage efficiency by providing compression as an option. This approach achieves full-functionality transaction-time databases without requiring temporal extensions in XML or database standards, and provides critical support to emerging application areas such as RFID.