Temporal Entity-Relationship Models -- a Survey <s> Introduction <s> I. CONCEPTUAL DATABASE DESIGN. 1. An Introduction to Database Design. 2. Data Modeling Concepts. 3. Methodologies for Conceptual Design. 4. View Design. 5. View Integration. 6. Improving the Quality of a Database Schema. 7. Schema Documentation and Maintenance. II. FUNCTIONAL ANALYSIS FOR DATABASE DESIGN. 1. Functional Analysis Using the Dataflow Model. 2. Joint Data and Functional Analysis. 3. Case Study. III. LOGICAL DESIGN AND DESIGN TOOLS. 1. High-Level Logical Design Using the Entity-Relationship Model. 2. Logical Design for the Relational Model. 3. Logical Design for the Network Model. 4. Logical Design for the Hierarchical Model. 5. Database Design Tools. Index. 0805302441T04062001 <s> BIB001 </s> Temporal Entity-Relationship Models -- a Survey <s> Introduction <s> This glossary contains concepts specific to temporal databases that are well-defined, well understood, and widely used. In addition to defining and naming the concepts, the glossary also explains the decisions made. It lists competing alternatives and discusses the pros and cons of these. It also includes evaluation criteria for the naming of concepts. This paper is a structured presentation of the results of e-mail discussions initiated during the preparation of the first book on temporal databases, Temporal Databases: Theory, Design, and Implementation, published by Benjamin Cummings, to appear January 1993. Independently of the book, an initiative aimed at designing a consensus Temporal SQL is under way. The paper is a contribution towards establishing common terminology, an initial subtask of this initiative. <s> BIB002
The Entity-Relationship (ER) Model 2] , in its di erent v ersions, with varying syntax and with some semantic variations, is enjoying a remarkable, and increasing, popularity in both the research community and in industry. The model is easy to comprehend and use. An ER diagram provides a good overview of database design, and the model's focus on the structural aspects of database schemas, as opposed to their behavioral aspects, also appears to match the levels of ambition for documentation adopted by many u s e r s . The ER model may be used for di erent but related purposes, namely for analysis|i.e., for modeling a mini-world|and for design|i.e., for describing the database schema of the a computer system. As a third alternative, the ER model may besupported directly by a DBMS. In that case, it may beused as an implementation model. However, although graphical and textual ER query languages have beenproposed by the research community, the ER model is rarely used as an implementation model. Rather, the typical use seems to be one where the model is used primarily for design, with the design diagrams also serving as analysis diagrams, and where the constructed diagrams are mapped to a relational platform. In step with the increasing di usion of relational platforms in industry, E R modeling is growing in popularity. The use of ER modeling is supported by a wealth of textbook material. For example, most introductory database textbooks (e.g.,    ) contain chapters on ER modeling, and several complete books exist (e.g., BIB001  ) that are devoted entirely to ER modeling. Companies either develop their own ER diagrams from scratch, or they purchase and modify generic, standard diagrams 2 . Indeed, generic diagrams for a variety of types of applications are commercially available, e.g., the FSDM from IBM. Some companies build ER diagrams using only simple drawing tools. Other companies use one of the many commercially available tools that are more sophisticated and bettersupport the building of diagrams and also map diagrams to implementation platforms. Such tools are either stand-alone, e.g., SmartER from Knowledge Based Systems, Inc. and ER/1 from Embarcadero Technologies, or are integrated parts of larger CASE tools, e.g., Teamwork/IM SQL from Cayenne Software, Inc. and Visible Analyst Workbench from Visible Systems Corporation. Typical implementation platforms include those provided by major SQL-based database systems. In the research community as well as in industry, it has beenrecognized that temporal aspects of database schemas are bothprominent and di cult to capture using the ER model. Put simply, when modeling fully the temporal aspects, the temporal aspects tend to obscure and clutter otherwise intuitive and easy-to-comprehend diagrams. As a result, some industrial users simply choose to ignore all temporal aspects in their ER diagrams and supplement the diagrams with phrases such as \full temporal support." The result is that the mapping of ER diagrams to relational tables must be performed by hand and the ER diagrams do not document well the temporally extended relational database schemas used by the application programmers. The research community's response has been to develop temporally enhanced ER models. Indeed, almost a dozen such models have beenreported in the research literature. Their informative names include the Temporal Enhanced Entity Relationship model   , the Temporal Entity Relationship model  , and the Relationship, Attribute, Keys, and Entities model  , to name but a few. Two general, orthogonal temporal aspects have received widespread attention, namely valid time and transaction time BIB002 . The valid time of a database fact is the time when the fact is true in the miniworld. ( We use the term \mini-world" for the part of reality that the database under consideration stores information about.) Thus, all database facts have an associated valid time. Di erent time types may be used when modeling the valid-time aspect, e.g., single time instants, intervals, or sets of intervals. Perhaps more importantly, the valid time may o r m a y not be captured explicitly in the database| this is the choice of the database designer. In ER models, unlike in the relational model, a database is not structured as a collection of facts, but rather as a set of entities and relationships with attributes. Thus, the valid times are associated only indirectly with facts. As an example, consider an Employee entity \E1" with a Department attribute. A v alid time of June 1996 associated with value \Shipping" does not say t h a t \Shipping" is valid during June 1996, but rather that the fact \E1 is in Shipping" is valid during June 1996. Thus, when valid time is captured for an attribute such as Department, the database will record the varying Department values for the Employee entities. If it is not captured, the database will record only one department v alue for each Employee entity. Orthogonal to valid time, the transaction time of a database fact is the time when the fact is current in the database and may be retrieved. Unlike valid time, transaction time may be associated with any structure stored in a database, not only with facts. Still, all structures stored in a database have a transaction-time aspect. And again, this aspect may or may not, at the designers discretion, be captured in the database. The transaction-time aspect has a duration: from insertion to (logical) deletion. In addition to valid and transaction time, a data model may support arbitrary time attributes with no built-in semantics in the data model. For employee entities, such attributes could record birth dates, hiring dates, etc. A data model that supports such time attributes is said to support user-de ned time. In summary, facts stored in a database have a valid time and a transaction time, although those times may not be explicitly recorded 15] . We s a y that a data model supports a temporal aspect, i.e., valid or transaction time, if it provides built-in means for indicating where in an ER diagram this aspect should be captured. The temporal ER models attempt to more naturally and elegantly model the temporal aspects, such a s v alid and transaction time, of information by c hanging the semantics of the ER model or by adding new constructs to the model. The models take quite di erent approaches to adding built-in temporal support to the ER model. This paper is the rst to survey all known (to the authors!) temporal ER models. In addition, the paper provides a comprehensive list of possible properties of temporal ER models, and it characterizes the models according to those properties. With about a dozen models having been proposed over the past 15 years, such a survey is in order. It consolidates in a single and easy-to-access source the central ideas, concepts, and insights achieved in temporal ER modeling. The survey makes it easier for future research and development to maximally build on, bene tfrom, and extend past results. Thus, the survey is aimed at researchers and practitioners interested in temporal data modeling and data model design. Four studies are somewhat related to or complement the study reported here. Theodoulidis and Loucopoulos 34] describe and compare nine approaches to specify and use time in conceptual modeling, here viewed as both semantic data modeling and requirement speci cation, of information systems. Their study includes only two of the ER models surveyed here. The comparison of the models fall in three parts and classi es the models in terms of time semantics, model semantics, and temporal functionalities. Our criteria also characterize the models in terms of user-friendliness. The primary focus of their paper is the examination of the ontology and properties of time in the context of information systems, whereas our focus is the examination of how the extensions of the ER model into temporal ER models are shaped. McKenzie and Snodgrass 22] survey and evaluate twelve di erent temporal extensions of the relational algebra. They evaluate the algebras against 26 design criteria. These criteria are mainly concerned with the properties of the data objects|temporal relations and their components|that the algebras manipulate and with the properties of the algebraic operators themselves. While their survey concerns internal algebras, our survey concerns notations for conceptual modeling. In addition, our focus is on the properties of the structural aspects of the temporal ER models. Without coauthors, Snodgrass has also conducted a critical comparison of temporal object-oriented data models 28] . While ER models do incorporate some structural object-oriented features, our study does not consider object-oriented models for that, we instead refer the reader to Snodgrass' study. Also unlike our study that emphasizes structural aspects, Snodgrass' study focused on the models' query languages, i.e., on behavioral aspects. Roddick a n d P atrick 24] survey the progress of incorporating time in various data models at the conceptual and, primarily, logical level of database modeling, and in arti cial intelligence. The work describes nine di erent properties of temporal modeling systems, but unlike our survey they do not evaluate the models against the properties described. Their broad study brie y covers two of the temporal ER models in our study. The descriptions in the literature of the di erent models use diverse and, at times, incompatible and con icting terminology. In this survey, we adopt the coherent terminology of the temporal database glossary 15] when possible. In addition, the original de nitions of the models are often informal and rely on the reader's intuition. In part also to achieve a homogeneous survey of a manageable size, we will give informal descriptions of some aspects. Further, we will emphasize the common core of features of the temporal ER models: the use of ER modeling to capture the structural aspects of a database schema. We will not cover behavioral aspects such as query and rule languages in detail. The paper is structured as follows. Section 2 provides an overview of all temporal ER models known to the authors. Section 3 then identi es a set of 20 evaluation criteria and evaluates each model according to these criteria. Finally in Section 4, a conclusion and a discussion of future work is given.