Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
Petri-net-based hypertext: document structure with browsing semantics
ACM Transactions on Information Systems (TOIS)
Hypertext and hypermedia
A logic for reasoning about probabilities
Information and Computation - Selections from 1988 IEEE symposium on logic in computer science
A catalog of complexity classes
Handbook of theoretical computer science (vol. A)
Handbook of theoretical computer science (vol. B)
Handbook of theoretical computer science (vol. B)
Hyperdocuments as automata: trace-based browsing property verification
ECHT '92 Proceedings of the ACM conference on Hypertext
Navigating in hyperspace: designing a structure-based toolbox
Communications of the ACM
Discrete and Combinatoral Mathematics: An Applied Introduction 2nd Ed.
Discrete and Combinatoral Mathematics: An Applied Introduction 2nd Ed.
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Introduction to probabilistic automata (Computer science and applied mathematics)
Introduction to probabilistic automata (Computer science and applied mathematics)
An Heuristic to Capture Longer User Web Navigation Patterns
EC-WEB '00 Proceedings of the First International Conference on Electronic Commerce and Web Technologies
Data Mining of User Navigation Patterns
WEBKDD '99 Revised Papers from the International Workshop on Web Usage Analysis and User Profiling
Query similarity by projecting the query-flow graph
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Preprocessing time series data for classification with application to CRM
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Multiple vehicles for a semantic navigation across hyper-environments
ESWC'05 Proceedings of the Second European conference on The Semantic Web: research and Applications
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One of the main unsolved problems confronting Hypertext is the navigation problem, namely the problem of having to know where you are in the database graph representing the structure of a Hypertext database, and knowing how to get to some other place you are searching for in the database graph. Previously we formalised a Hypertext database in terms of a directed graph whose nodes represent pages of information. The notion of a trail, which is a path in the database graph describing some logical association amongst the pages in the trail, is central to our model. We defined a Hypertext Query Language, HQL, over Hypertext databases and showed that in general the navigation problem, i.e. the problem of finding a trail that satisfies a HQL query (technically known as the model checking problem), is NP-complete. Herein we present a preliminary investigation of using a probabilistic approach in order to enhance the efficiency of model checking. The flavour of our investigation is that if we have some additional statistical information about the Hypertext database then we can utilise such information during query processing. We present two different approaches. The first approach utilises the theory of probabilistic automata. In this approach we view a Hypertext database as a probabilistic automaton, which we call a Hypertext probabilistic automaton. In such an automaton we assume that the probability of traversing a link is determined by the usage statistics of that link. We exhibit a special case when the number of trails that satisfy a query is always finite and indicate how to give a finite approximation of answering a query in the general case. The second approach utilises the theory of random Turing machines. In this approach we view a Hypertext database as a probabilistic algorithm, realised via a Hypertext random automaton. In such an automaton we assume that out of a choice of links, traversing any one of them is equally likely. We obtain the lower bound of the probability that a random trail satisfies a query. In principle, by iterating this probabilistic algorithm, associated with the Hypertext database, the probability of finding a trail that satisfies the query can be made arbitrarily large.