Theoretical framework of temporal databases.

by Lam Wing Hee.Thesis (M.Phil.)--Chinese University of Hong Kong, 1991.Bibliography: leaves [56]-59.List of Figures --- p.vAcknowledgements --- p.viChapter 1. --- Introduction --- p.1Chapter 1.1 --- Historical Data and Temporal Databases --- p.1Chapter 1.2 --- Valid Time and Transaction Time --- p.3Chapter 1.2.1 --- Snapshot Databases --- p.3Chapter 1.2.2 --- Rollback Databases --- p.4Chapter 1.2.3 --- Historical Databases --- p.6Chapter 1.2.4 --- Temporal Databases --- p.7Chapter 1.3 --- Literature Review --- p.8Chapter 1.3.1 --- Data Models --- p.9Chapter 1.3.2 --- Query Languages --- p.11Chapter 1.3.3 --- Logical Design --- p.13Chapter 2. --- The Temporal Relational Data Model --- p.14Chapter 2.1 --- The Temporal Relational Data Model - Informal Description --- p.14Chapter 2.2 --- The Temporal Relational Data Model - Formal Description --- p.15Chapter 2.2.1 --- Valid and Transaction Time Intervals --- p.16Chapter 2.2.2 --- "Attributes, Tuples and Temporal Relations" --- p.16Chapter 2.3 --- What is a Key in Temporal Relations? --- p.17Chapter 3. --- The Temporal Relational Algebra --- p.20Chapter 3.1 --- Operations in the Temporal Relational Algebra --- p.20Chapter 3.1.1 --- Union and Set Difference --- p.21Chapter 3.1.2 --- Selection --- p.21Chapter 3.1.3 --- Projection --- p.23Chapter 3.1.4 --- Join --- p.24Chapter 3.1.4.1 --- Natural Join --- p.25Chapter 3.2 --- Temporal Relational Algebra and TempSQL --- p.30Chapter 4. --- Classical Data Dependencies in Temporal Relations --- p.32Chapter 4.1 --- Functional Dependency in the Temporal Relational Model --- p.32Chapter 4.2 --- Multivalued Dependency in the Temporal Relational Model --- p.33Chapter 4.3 --- Relationship with Snapshot Data Dependencies --- p.34Chapter 4.4 --- Lossless Decomposition --- p.35Chapter 5. --- Asynchronous Dependency --- p.39Chapter 5.1 --- Asynchronous Dependency --- p.40Chapter 5.2 --- Asynchronous Normal Form --- p.41Chapter 5.3 --- Generalized Form of Data Dependency --- p.42Chapter 5.3.1 --- Embedded Implicational Dependency --- p.43Chapter 5.3.2 --- Algebraic Dependency --- p.45Chapter 5.4 --- Asynchronous Dependency versus Synchronous Dependency --- p.46Chapter 6. --- Conclusions --- p.48Chapter 6.1 --- Summary of the Thesis --- p.48Chapter 6.2 --- Unsolved Problems and Research Directions --- p.49Chapter 6.2.1 --- Equivalent Representations in the Temporal Relational Model --- p.49Chapter 6.2.2 --- The Notion of 'Completeness' of Temporal Query Languages --- p.50Chapter 6.2.3 --- Logical Basis for Temporal Data Models and Languages --- p.51Chapter 6.2.4 --- Other Temporal Dependencies --- p.51Chapter 6.2.5 --- Research Directions in Topics other than Theory --- p.52Appendix Proofs of Theorems --- p.53Bibliography --- p.5

GenTKG: Generative Forecasting on Temporal Knowledge Graph

The rapid advancements in large language models (LLMs) have ignited interest in the temporal knowledge graph (tKG) domain, where conventional carefully designed embedding-based and rule-based models dominate. The question remains open of whether pre-trained LLMs can understand structured temporal relational data and replace them as the foundation model for temporal relational forecasting. Therefore, we bring temporal knowledge forecasting into the generative setting. However, challenges occur in the huge chasms between complex temporal graph data structure and sequential natural expressions LLMs can handle, and between the enormous data sizes of tKGs and heavy computation costs of finetuning LLMs. To address these challenges, we propose a novel retrieval augmented generation framework that performs generative forecasting on tKGs named GenTKG, which combines a temporal logical rule-based retrieval strategy and lightweight parameter-efficient instruction tuning. Extensive experiments have shown that GenTKG outperforms conventional methods of temporal relational forecasting under low computation resources. GenTKG also highlights remarkable transferability with exceeding performance on unseen datasets without re-training. Our work reveals the huge potential of LLMs in the tKG domain and opens a new frontier for generative forecasting on tKGs.Comment: 8 pages, accepted to Temporal Graph Learning @ NeurIPS 202

Object-relational spatio-temporal databases

We present an object-relational model for uniform handling of dimensional data. Spatial, temporal, spatio-temporal and ordinary data are special cases of dimensional data. The said uniformity is achieved through the concept of dimension alignment, which automatically allows lower dimensional data and queries to be used in a higher dimensional context;Unlike ordinary data, dimensional objects are interwoven. We introduce object identity (oid) fragments to circumvent data redundancy at logical level. Computed types are placed appropriately in a type hierarchy to allow maximal use of existing methods. A query language for spatio-temporal data is presented for associative navigation. A framework for algebraic optimization of the query language is suggested;A pattern matching language is designed for complex querying of spatio-temporal data which seamlessly extends the associative navigation in our query language. The pattern matching language recognizes special features of time and space providing an appropriate level of abstraction for application development compared to traditional languages. This reduces the need for embedding the query language in a lower level language such as C++. The pattern matching language is also dimensionally extensible. The pattern matching allows query of data with multiple granularities and continuous data. It also provides hooks for direct query of scientific data (observations);Our model is dimensionally extensible, and also an extension of a relational model for dimensional data. Moreover the dimensionality and addition of oids are mutually orthogonal concepts. Thus starting from classical ordinary data, one may migrate to higher forms of relational or object-relational data in any sequence, without having to recode application software. Our model does not deal with complex objects, which is left as a future extension

ON THE SEMANTICS OF TRANSACTION TIME AND VALID TIME IN BITEMPORAL DATABASES

Numerous proposals for extending the relational data model to incorporate the temporal dimension of data have appeared in the past several years. While most of these have been historical databases, incorporating in some fashion a valid time dimension to the data model and the query languages, others have been rollback databases, incorporating a transaction time dimension, or bitemporal databases, incorporating both of these temporal dimensions. In this paper we address an issue that has been lacking in many of these papers, namely, a formal specification of the precise semantics of these temporal dimensions of data. We introduce the notion of reference time - the time that any operation is applied to the database state - and provide a logical analysis of the interrelationships among these three temporal dimensions. We also provide an analysis of the meaning of various variables such as now and â which have been used in many of these models without a complete specification of their semantics.Information Systems Working Papers Serie

The representation of time in data warehouses

This thesis researches the problems concerning the specification and implementation of the temporal requirements in data warehouses. The thesis focuses on two areas, firstly, the methods for identifying and capturing the business information needs and associated temporal requirements at the conceptual level and; secondly, methods for classifying and implementing the requirements at the logical level using the relational model. At the conceptual level, eight candidate methodologies were investigated to examine their suitability for the creation of data models that are appropriate for a data warehouse. The methods were evaluated to assess their representation of time, their ability to reflect the dimensional nature of data warehouse models and their simplicity of use. The research found that none of the methods under review fully satisfied the criteria. At the logical level, the research concluded that the methods widely used in current practice result in data structures that are either incapable of answering some very basic questions involving history or that return inaccurate results. Specific proposals are made in three areas. Firstly, a new conceptual model is described that is designed to capture the information requirements for dimensional models and has full support for time. Secondly, a new approach at the logical level is proposed. It provides the data structures that enable the requirements captured in the conceptual model to be implemented, thus enabling the historical questions to be answered simply and accurately. Thirdly, a set of rules is developed to help minimise the inaccuracy caused by time. A guide has been produced that provides practitioners with the tools and instructions on how to implement data warehouses using the methods developed in the thesis

Exploring sensor data management

The increasing availability of cheap, small, low-power sensor hardware and the ubiquity of wired and wireless networks has led to the prediction that `smart evironments' will emerge in the near future. The sensors in these environments collect detailed information about the situation people are in, which is used to enhance information-processing applications that are present on their mobile and `ambient' devices.\ud \ud Bridging the gap between sensor data and application information poses new requirements to data management. This report discusses what these requirements are and documents ongoing research that explores ways of thinking about data management suited to these new requirements: a more sophisticated control flow model, data models that incorporate time, and ways to deal with the uncertainty in sensor data

Snapshot Semantics for Temporal Multiset Relations (Extended Version)

Snapshot semantics is widely used for evaluating queries over temporal data: temporal relations are seen as sequences of snapshot relations, and queries are evaluated at each snapshot. In this work, we demonstrate that current approaches for snapshot semantics over interval-timestamped multiset relations are subject to two bugs regarding snapshot aggregation and bag difference. We introduce a novel temporal data model based on K-relations that overcomes these bugs and prove it to correctly encode snapshot semantics. Furthermore, we present an efficient implementation of our model as a database middleware and demonstrate experimentally that our approach is competitive with native implementations and significantly outperforms such implementations on queries that involve aggregation.Comment: extended version of PVLDB pape