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Noord, Gertjan van; Gerdemann, Dale

doi:10.1023/a:1012291501330

Cited by 52 publications

(10 citation statements)

References 21 publications

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“…Another model that is of interest for CER is the symbolic automaton, which allows CER patterns to apply constraints on the attributes of events. Automata that have predicates on their transitions were already proposed in [26]. This initial idea has recently been expanded and more fully investigated in symbolic automata [27,28,9].…”

Section: Related Workmentioning

confidence: 99%

Symbolic Register Automata for Complex Event Recognition and Forecasting

Alevizos¹,

Artikis²,

Παλιούρας³

2021

Preprint

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We propose an automaton model which is a combination of symbolic and register automata, i.e., we enrich symbolic automata with memory. We call such automata Symbolic Register Automata (SRA). SRA extend the expressive power of symbolic automata, by allowing Boolean formulas to be applied not only to the last element read from the input string, but to multiple elements, stored in their registers. SRA also extend register automata, by allowing arbitrary Boolean formulas, besides equality predicates. We study the closure properties of SRA under union, intersection, concatenation, Kleene closure, complement and determinization and show that SRA, contrary to symbolic automata, are not in general closed under complement and they are not determinizable. However, they are closed under these operations when a window operator, quintessential in Complex Event Recognition, is used. We show how SRA can be used in Complex Event Recognition in order to detect patterns upon streams of events, using our framework that provides declarative and compositional semantics, and that allows for a systematic treatment of such automata. We also show how the behavior of SRA, as they consume streams

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Section: Related Workmentioning

confidence: 99%

Symbolic Register Automata for Complex Event Recognition and Forecasting

Alevizos¹,

Artikis²,

Παλιούρας³

2021

Preprint

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“…For the following discussions on the application of model-based diagnosis concepts to the automated debugging of recommender user interface descriptions we introduce a variant of Predicate-Based Finite State Automata (PFSA) [15] (see e.g. Figure 1) which is a natural and compact approach to define state transitions (transitions are defined in terms of domain restrictions of finite domain variables).…”

Section: Finite State Models Of Recommender User Interfacesmentioning

confidence: 99%

Diagnosing Faulty Transitions in Recommender User Interface Descriptions

Felfernig

2006

Advances in Applied Artificial Intelligence

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Abstract. Complex assortments of products and services offered by online selling platforms require the provision of sales support systems assisting customers in the product selection process. Knowledge-based recommenders are intelligent sales assistance systems which guide online customers through personalized sales dialogs and automatically determine products which conform to their needs and wishes. Such systems have been successfully applied in a number of application domains such as financial services or digital cameras. In this context, the construction of recommender user interfaces is still a challenging task. In many cases faulty models of recommender user interfaces are defined by knowledge engineers and no automated support for debugging such models is available. In this paper we discuss a formal model for defining the intended behaviour of recommender user interfaces and show the application of modelbased diagnosis concepts which allow the automated debugging of those definitions. Experiences show that this approach significantly increases the productivity of recommender user interface development and maintenance.

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“…Such process definitions are based on a predicate augmented finite state recognizer (PFSR) [24] (constraints describe transitions between different states of a recommender process) which represents allowed navigation paths within an advisor (see Figure 2). Transition conditions between states of a recommender process are evaluated using the Koba4MS constraint engine.…”

Section: Process Designermentioning

confidence: 99%

Koba4MS: Selling Complex Products and Services Using Knowledge-Based Recommender Technologies

Felfernig

Seventh IEEE International Conference on E-Commerce Technology (CEC'05)

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Untitled

Cited by 52 publications

References 21 publications

Symbolic Register Automata for Complex Event Recognition and Forecasting

Symbolic Register Automata for Complex Event Recognition and Forecasting

Diagnosing Faulty Transitions in Recommender User Interface Descriptions

Koba4MS: Selling Complex Products and Services Using Knowledge-Based Recommender Technologies

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