TEMPOS: A Platform for Developing Temporal Applications on Top of Object DBMS

This paper presents TEMPOS: a set of models and languages supporting the manipulation of temporal data on top of object DBMS. The proposed models exploit object-oriented technology to meet some important, yet traditionally neglected design criteria related to legacy code migration and representation independence. Two complementary ways for accessing temporal data are offered: a query language and a visual browser. The query language, namely TempOQL, is an extension of OQL supporting the manipulation of histories regardless of their representations, through fully composable functional operators. The visual browser offers operators that facilitate several time-related interactive navigation tasks, such as studying a snapshot of a collection of objects at a given instant, or detecting and examining changes within temporal attributes and relationships. TEMPOS models and languages have been formalized both at the syntactical and the semantical level and have been implemented on top of an object DBMS. The suitability of the proposals with regard to applications' requirements has been validated through concrete case studies

Templates for Supporting Sequenced Temporal Semantics in Pig Latin

This report describes proposed templates for supporting sequenced temporal semantics in Pig Latin, a dataflow language used primarily for the analysis of very large data sets. Sequence semantics says that if we take a relation and divide it into smaller relations based on timestamps, while still carrying out the regular Pig Latin program over it, the result should be the same as when carrying out the temporal Pig Latin program over the original relation. In real time, the relations can be enormous, and dividing such relations into smaller ones based on every possible timestamp creates an extremely large number of smaller relations. Hence, we create temporal programs, which eliminates the need to divide a relation into smaller relations and carry out additional operations over those smaller relations. We look at each of the templates and discuss their functionality. One example of such a template is temporal grouping, which provides an ability to group a set of tuples or a whole relation based on timestamps. Using temporal grouping, a user can find the number of tuples that exist at a given point of time. Another example is temporal coalescing, which allows a user to project multiple tuples and the timestamps of their existence in the database. We compare the complexity of the templates with the existing operations

Stream Reasoning in Temporal Datalog

In recent years, there has been an increasing interest in extending traditional stream processing engines with logical, rule-based, reasoning capabilities. This poses significant theoretical and practical challenges since rules can derive new information and propagate it both towards past and future time points; as a result, streamed query answers can depend on data that has not yet been received, as well as on data that arrived far in the past. Stream reasoning algorithms, however, must be able to stream out query answers as soon as possible, and can only keep a limited number of previous input facts in memory. In this paper, we propose novel reasoning problems to deal with these challenges, and study their computational properties on Datalog extended with a temporal sort and the successor function (a core rule-based language for stream reasoning applications)

A survey of temporal knowledge discovery paradigms and methods

With the increase in the size of data sets, data mining has recently become an important research topic and is receiving substantial interest from both academia and industry. At the same time, interest in temporal databases has been increasing and a growing number of both prototype and implemented systems are using an enhanced temporal understanding to explain aspects of behavior associated with the implicit time-varying nature of the universe. This paper investigates the confluence of these two areas, surveys the work to date, and explores the issues involved and the outstanding problems in temporal data mining

How Musical Oddballs Warp Psychological Time

Oddballs—low-probability, attention-capturing expectancy violations—are judged as longer than non-oddballs, but are temporal intervals that contain oddballs judged as longer than those that do not? In 2 experiments, we tested competing model predictions using a novel and covert measure of subjective duration—musical imagery reproduction. Participants verbally estimated and reproduced with musical imagery repeated, coherent, or incoherent familiar or unfamiliar chord sequences (3.5 s, 7 s, or 12 s) that either did or did not contain dynamic auditory oddballs. Participants verbally estimated repeated chord sequences that contained oddballs as shorter than those that did not, but reproduced with musical imagery incoherent chord sequences that contained oddballs as longer than those that did not. These findings suggest that (a) intervals that contain attention-capturing, high-priority events are judged as shorter than those that do not when people are engaged in relatively temporal information processing, but as longer than those that do not when people are engaged in relatively nontemporal information processing, and (b) temporal and nontemporal information processing are interdependent. These results support the resource allocation model of short interval time estimation. We discuss implications for attention- and memory-based models, dynamic attending theory, and the ongoing debate about the mechanisms driving the temporal oddball illusion

A logic with temporally accessible iteration

Deficiency in expressive power of the first-order logic has led to developing its numerous extensions by fixed point operators, such as Least Fixed-Point (LFP), inflationary fixed-point (IFP), partial fixed-point (PFP), etc. These logics have been extensively studied in finite model theory, database theory, descriptive complexity. In this paper we introduce unifying framework, the logic with iteration operator, in which iteration steps may be accessed by temporal logic formulae. We show that proposed logic FO+TAI subsumes all mentioned fixed point extensions as well as many other fixed point logics as natural fragments. On the other hand we show that over finite structures FO+TAI is no more expressive than FO+PFP. Further we show that adding the same machinery to the logic of monotone inductions (FO+LFP) does not increase its expressive power either