Curtis E. Dyreson

A glossary of time granularity concepts

This paper is an extension of the precding glossary, but focussed on time granularity concepts. We use the same structure as in the previous glossary.

On the semantics of “now” in databases

Although “<italic>now</italic>” is expressed in SQL and CURRENT_TIMESTAMP within queries, this value cannot be stored in the database. How ever, this notion of an ever-increasing current-time value has been reflected in some temporal data models by inclusion of database-resident variables, such as “<italic>now</italic>” “<italic>until-changed,</italic> ” “**,” “@,” and “-”. Time variables are very desirable, but their used also leads to a new type of database, consisting of tuples with variables, termed a <italic>variable database.</italic>

Supporting valid-time indeterminacy

In valid-time indeterminacy it is known that an event stored in a database did in fact occur, but it is not known exactly when. In this paper we extend the SQL data model and query language to support valid-time indeterminacy. We represent the occurrence time of an event with a set of possible instants, delimiting when the event might have occurred, and a probability distribution over that set. We also describe query language constructs to retrieve information in the presence of indeterminacy. These constructs enable users to specify their credibility in the underlying data and their plausibility in the relationships among that data. A denotational semantics for SQLs select statement with optional credibility and plausibility constructs is given. We show that this semantics is reliable, in that it never produces incorrect information, is maximal, in that if it were extended to be more informative, the results may not be reliable, and reduces to the previous semantics when there is no indeterminacy. Although the extended data model and query language provide needed modeling capabilities, these extensions appear initially to carry a significant execution cost. A contribution of this paper is to demonstrate that our approach is useful and practical. An efficient representation of valid-time indeterminacy and efficient query processing algorithms are provided. The cost of support for indeterminacy is empirically measured, and is shown to be modest. Finally, we show that the approach is general, by applying it to the temporal query language constructs being proposed for SQL3.

Efficiently supporting temporal granularities

Granularity is an integral feature of temporal data. For instance, a persons age is commonly given to the granularity of years and the time of their next airline flight to the granularity of minutes. A granularity creates a discrete image, in terms of granules, of a (possibly continuous) time-line. We present a formal model for granularity in temporal operations that is integrated with temporal indeterminacy, or dont know when information. We also minimally extend the syntax and semantics of SQL-92 to support mixed granularities. This support rests on two operations, scale and cast, that move times between granularities, e.g., from days to months. We demonstrate that our solution is practical by showing how granularities can be specified in a modular fashion, and by outlining a time- and space-efficient implementation. The implementation uses several optimization strategies to mitigate the expense of accommodating multiple granularities.

Valid-time indeterminacy

In valid-time indeterminacy, it is known that an event stored in a temporal database did in fact occur, but it is not known exactly when the event occurred. An extension of the tuple-timestamped temporal data model, called the possible chronons data model, is presented to support valid-time indeterminacy. In the possible chronons data model, each event is represented with a set of possible chronons, delimiting when the event might have occurred and a probability distribution over the set. The TQuel query language is extended using constructs that specify the users credibility in the underlying valid-time data and the users plausibility in the relationships among that data. A formal tuple calculus semantics is outlined, and it is shown that this semantics reduces to the determinate semantics on determinate data.<<ETX>>

A Bibliography on Uncertainty Management in Information Systems

This is an evolving bibliography of documents on uncertainty and imprecision in information systems. By uncertainty and imprecision, we mean the representation of and query support for information that is fuzzy, unknown, partially known, vague, uncertain, probabilistic, indefinite, disjunctive, possible, maybe, incomplete, approximate, erroneous, or imprecise. Currently, the bibliography concentrates almost exclusively on database and knowledge-base systems, with few bl]References on other kinds of information systems.

Timestamp semantics and representation

Abstract Many database management systems and operating systems provide support for time values. At the physical level time values are known as timestamps. A timestamp has a physical realization and a temporal interpretation. The physical realization is a pattern of bits while the temporal interpretation is the meaning of each bit pattern, that is, the time each pattern represents. All previous proposals defined timestamps in terms of seconds. However, as we show, there are at least seven definitions of this fundamental time unit. We propose a more precise temporal interpretation, the time-line clock, that constructs a time-line by using different well-defined clocks in different periods. We also propose timestamp formats for events, intervals and spans. These formats can represent all of time to the granularity of a second, and all of recorded history to a finer granularity of a microsecond. Our proposed formats were designed to be more space and time efficient than existing representations. We compare our formats with those used in common operating systems and database management systems.

The Jungle database search engine

Information spread in in databases cannot be found by current search engines. A database search engine is capable to access and advertise database on the WWW. Jungle is a database search engine prototype developed at Aalborg University. Operating through JDBC connections to remote databases, Jungle extracts and indexes database data and meta-data, building a data store of database information. This information is used to evaluate and optimize queries in the AQUA query language. AQUA is a natural and intuitive database query language that helps users to search for information without knowing how that information is structured. This paper gives an overview of AQUA and describes the implementation of Jungle.

Efficient timestamp input and output

In this paper we provide efficient algorithms for converting between timestamp values that signify some number of seconds from an arbitrary origin, and character strings specifying Gregorian dates, such as ‘January 1, 1993’. We give several algorithms that explore a range of time and space trade‐offs. Unlike previous algorithms, those discussed here have a constant time cost over a greatly extended range of timestamp values. These algorithms are especially useful in operating systems and in database management systems.