Incremental view-based analysis of stock market data streams

Behrend, Andreas; Dorau, Christian; Manthey, Rainer; Schueller, Gereon

doi:10.1145/1451940.1451978

Cited by 8 publications

(3 citation statements)

References 16 publications

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“…The relevance and quality of the transformation-based approaches, however, has been already shown in various practical research projects (e.g. [5,8]) at the University of Bonn.…”

Section: Discussionmentioning

confidence: 99%

A Uniform Fixpoint Approach to the Implementation of Inference Methods for Deductive Databases

Behrend

2013

Lecture Notes in Computer Science

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Within the research area of deductive databases three different database tasks have been deeply investigated: query evaluation, update propagation and view updating. Over the last thirty years various inference mechanisms have been proposed for realizing these main functionalities of a rule-based system. However, these inference mechanisms have been rarely used in commercial DB systems until now. One important reason for this is the lack of a uniform approach well-suited for implementation in an SQL-based system. In this paper, we present such a uniform approach in form of a new version of the soft consequence operator. Additionally, we present improved transformation-based approaches to query optimization and update propagation and view updating which are all using this operator as underlying evaluation mechanism. enhance existing methods to query optimization [3] as well as update propagation [6] and to develop a new approach to view updating. In order to handle alternative derivations that may occur in view updating methods, an extended version of the original soft consequence operator has to be developed. In this paper, this new version is presented, which is well-suited for efficiently determining the semantics of definite and indefinite databases but remains compatible with the set-oriented, bottom-up evaluation of SQL. Basic conceptsA Datalog rule is a function-free clause of the form H 1 ← L 1 ∧ · · · ∧ L m with m ≥ 1 where H 1 is an atom denoting the rule's head, and L 1 , . . . , L m are literals, i.e. positive or negative atoms, representing its body. We assume all deductive rules to be safe, i.e., all variables occurring in the head or in any negated literal of a rule must be also present in a positive literal in its body. If A ≡ p(t 1 , . . . , t n ) with n ≥ 0 is a literal, we use vars(A) to denote the set of variables occurring in A and pred(A) to refer to the predicate symbol p of A. If A is the head of a given rule R, we use pred(R) to refer to the predicate symbol of A. For a set of rules R, pred(R) is defined asa finite set of facts (called base facts), I is a finite set of integrity constraints (i.e.,positive ground atoms) and R a finite set of rules such that pred(F ) ∩ pred(R) = Ø and pred(I) ⊆ pred(F ∪ R). Within a deductive database D, a predicate symbol p is called derived (view predicate), if p ∈ pred(R). The predicate p is called extensional (or base predicate), if p ∈ pred(F ). Let H D be the Herbrand base of D = F , R, I . The set of all derivable literals from D is defined as the well-founded model [21] for (F ∪ R): M D := I + ∪ ¬ · I − where I + , I − ⊆ H D are sets of ground atoms and ¬ · I − includes all negations of atoms in I − . The set I + represents the positive portion of the well-founded model while ¬ · I − comprises all negative conclusions. The semantics of a database D = F , R, I is defined as the well-founded model M D := I + ∪ ¬ · I − for F ∪ R if all integrity constraints are satisfied in M D , i.e., I ⊆ I + .Otherwise, the semantics of D is undefined. For the sake of...

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“…The relevance and quality of the transformation-based approaches, however, has been already shown in various practical research projects (e.g. [5,8]) at the University of Bonn.…”

Section: Discussionmentioning

confidence: 99%

A Uniform Fixpoint Approach to the Implementation of Inference Methods for Deductive Databases

Behrend

2013

Lecture Notes in Computer Science

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“…It is also known as a momentum indicator as it computes the ratio of successive higher prices to successive lower prices [10]. The RSI is computed as a percentage ranged from 0% to 100%.…”

Section: B Relative Strength Indexmentioning

confidence: 99%

Detecting faults in technical indicator computations for financial market analysis

Sim

Low²,

Kuo

2010

The 2nd International Conference on Information Science and Engineering

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Many financial trading and charting software packages provide users with technical indicators to analyze and predict price movements in financial markets. Any computation fault in technical indicator may lead to wrong trading decisions and cause substantial financial losses. Testing is a major software engineering activity to detect computation faults in software. However, there are two problems in testing technical indicators in these software packages. Firstly, the indicator values are updated with real-time market data that cannot be generated arbitrarily. Secondly, technical indicators are computed based on a large amount of market data. Thus, it is extremely difficult, if not impossible, to derive the expected indicator values to check the correctness of the computed indicator values. In this paper, we address the above problems by proposing a new testing technique to detect faults in computation of technical indicators. We show that the proposed technique is effective in detecting computation faults in faulty technical indicators on the MetaTrader 4 Client Terminal.

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“…Nowadays, UP plays an important role in the context of the view-based analysis of data streams [7,13] and forms the basis for the incremental evaluation of continuous queries (e.g. in Oracle [28]).…”

Section: Introductionmentioning

confidence: 99%

A magic approach to optimizing incremental relational expressions

Behrend

2009

Proceedings of the 2009 International Database Engineering &Amp; Applications Symposium on - IDEAS '09

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This paper is concerned with a transformation-based approach to update propagation in an extended version of Codd's relational algebra which allows for defining derived relations (even recursively). It is shown that the desired optimization effects of update propagation may be lost if no generalized selection pushing strategy is employed to the transformed algebra expressions. A possible solution is the application of the Magic Sets rewriting but this may lead to unstratifiability of the incremental expressions. For the efficient evaluation of Magic Sets transformed algebra expressions we propose to use the soft stratification approach because of the simplicity and efficiency of this technique.

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Incremental view-based analysis of stock market data streams

Cited by 8 publications

References 16 publications

A Uniform Fixpoint Approach to the Implementation of Inference Methods for Deductive Databases

A Uniform Fixpoint Approach to the Implementation of Inference Methods for Deductive Databases

Detecting faults in technical indicator computations for financial market analysis

A magic approach to optimizing incremental relational expressions

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