The deductive database system [Lscr    ][Dscr    ][Lscr    ]++

Arni, Faiz; Ong, KayLiang; Tsur, Shalom; Wang, Haixun; Zaniolo, Carlo

doi:10.1017/s1471068402001515

Cited by 49 publications

(47 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As describe in [44] logical rule are very effective at (i) bringing the domain knowledge to bear upon specific mining task, (ii) driving the mining process by calling procedurally defined UDAs to perform the specific mining tasks, and (iii) combining the results of knowledge extraction with application-expert rules. From a research viewpoint, the success obtained in [45] with a rule-based data mining environment suggests the need for two important enhancements that were not available in the framework of systems [46] originally used in those experiments. One is the ability of using induced rules as if they were deductive rules, and the other is ability of using deductive rules to define UDAs which compare in terms of efficiency with those written in ATLaS SQL which approach those of UDAs written in a procedural language.…”

Section: Resultsmentioning

confidence: 99%

Mining Databases and Data Streams with Query Languages and Rules

Zaniolo¹

2006

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Abstract. Among data-intensive applications that are beyond the reach of traditional Data Base Management Systems (DBMS), data mining stands out because of practical importance and the complexity of the research problems that must be solved before the vision of Inductive DBMS can become a reality. In this paper, we first discuss technical developments that have occurred since the very notion of Inductive DBMS emerged as a result of the seminal papers authored by Imielinski and Mannila a decade ago. The research progress achieved since then can be subdivided into three main problem subareas as follows: (i) language (ii) optimization, and (iii) representation. We discuss the problems in these three areas and the different approaches to Inductive DBMS that are made possible by recent technical advances. Then, we pursue a languagecentric solution, and introduce simple SQL extensions that have proven very effective at supporting data mining. Finally, we turn our attention to the related problem of supporting data stream mining using Data Stream Management Systems (DSMS) and introduce the notion of Inductive DSMS. In addition to continuous query languages, DSMS provide support for synopses, sampling, load shedding, and other built-in functions that are needed for data stream mining. Moreover, we show that Inductive DSMS can be achieved by generalizing DSMS to assure that their continuous query languages support efficiently data stream mining applications. Thus, DSMS extended with inductive capabilities will provide a uniquely supportive environment for data stream mining applications.

show abstract

Section: Resultsmentioning

confidence: 99%

Mining Databases and Data Streams with Query Languages and Rules

Zaniolo¹

2006

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…set containment) and therefore can only be used in stratified programs, i.e., with the same constraints regulating the use of negation in programs. DeAL also supports XY-stratification that is a form of explicit (i.e., compile-time recognizable) local stratification that was first introduced by LDL++ [4] and recently proved quite useful in the parallelization of advanced analytics in MapReduce distributed execution environments [6]. The important novelty of DeAL however is that it introduces the two monotonic aggregates fsmax and fscnt that can be used freely in recursive definitions.…”

Section: Monotonic Aggregatesmentioning

confidence: 99%

“…Thus, the time needed to deliver a bicycle is the maximum of the number of days that the various basic parts require to arrive. We use the notation for aggregates that is used in LDL++ [4], and thus max denotes the usual non-monotonic aggregate that can also be expressed using negation. However fsmax denotes the new fs-aggregates introduced in [14] which are monotonic.…”

Section: The Fsmax Aggregatementioning

confidence: 99%

“…Even when expressed in XY-stratified rules [4], recursive programs with aggregates suffer from inefficient query evaluation performance. In this springtime for Datalog, a more general theory has sprouted to life [14], providing the formal basis for the very practical language and system extensions that are the focus of our demo.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we present our Deductive Application Language (DeAL) System 2 , a next-generation Datalog system we have developed at UCLA, building on earlier Deductive Database technology [4]. DeAL supports negation, built-in and user defined aggregates, and non-monotonic aggregation via XY-stratification, and more importantly, the new monotonic aggregate extensions discussed in Section 2.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Graph queries in a next-generation Datalog system

2013

Self Cite

View full text Add to dashboard Cite

Recent theoretical advances have enabled the use of special monotonic aggregates in recursion. These special aggregates make possible the concise expression and efficient implementation of a rich new set of advanced applications. Among these applications, graph queries are particularly important because of their pervasiveness in data intensive application areas. In this demonstration, we present our Deductive Application Language (DeAL) System, the first of a new generation of Deductive Database Systems that support applications that could not be expressed using regular stratification, or could be expressed using XY-stratification (also supported in DeAL) but suffer from inefficient execution. Using example queries, we will (i) show how complex graph queries can be concisely expressed using DeAL and (ii) illustrate the formal semantics and efficient implementation of these powerful new monotonic constructs.

show abstract

Deductive Databases

Grant¹,

Minker²

2007

Wiley Encyclopedia of Computer Science and Engineering

View full text Add to dashboard Cite

In this article define the concept of deductive database including the formal definition of a query and an answer to a query; we provide an historical background for deductive databases; we explain the different types of deductive databases starting with Datalog and including stratified, nonstratified, extended, and disjunctive deductive databases; we discuss various semantics for deductive databases; we list the prototypes and implementations of deductive databases; we also give several applications that cannot be handled by relational and databases and require deductive databases.

show abstract

The deductive database system [Lscr ][Dscr ][Lscr ]++

Cited by 49 publications

References 40 publications

Mining Databases and Data Streams with Query Languages and Rules

Mining Databases and Data Streams with Query Languages and Rules

Graph queries in a next-generation Datalog system

Deductive Databases

Contact Info

Product

Resources

About