Estimation of Uncertain Relations between Indeterminate Temporal Points

  title={Estimation of Uncertain Relations between Indeterminate Temporal Points},
  author={Vladimir Ryabov and Seppo Puuronen},
Many database applications need to manage temporal information and sometimes to estimate relations between indeterminate temporal points. Indeterminacy means that we do not know exactly when a particular event happened. In this case, temporal points can be defined within some temporal intervals. Measurements of these intervals are not necessarily based on exactly synchronized clocks, and, therefore, possible measurement errors need to be taken into account when estimating the temporal relation… 
Sequential pattern mining with uncertain data
This work uses a motivating example of sequential pattern mining to illustrate how to incorporate uncertain information in the process of data mining, and uses possible world semantics to interpret two typical types of uncertainty: the tuple-level existential uncertainty and the attribute-level temporal uncertainty.
Handling imperfect temporal relations
Ryabov, Vladimir Handling Imperfect Temporal Relations Jyväskylasta Studies in Computing, 2002, 75 p.


Representation and Reasoning with Uncertain Temporal Relations
This work suggests representation which includes explicit probability values of the consistent and the inconsistent parts of a temporal relation, which can be used to derive the probability and percentage values for a relation between any two temporal points.
Supporting valid-time indeterminacy
This paper extends the SQL data model and query language to support valid-time indeterminacy, and represents the occurrence time of an event with a set of possible instants, delimiting when the event might have occurred, and a probability distribution over that set.
Exact and approximate reasoning about temporal relations 1
This paper addresses a fundamental reasoning task that arises in applications of the algebra: Given (possibly indefinite) knowledge about the relationships between intervals, find all feasible relationships between two intervals, called the minimal labels problem.
A probabilistic relational model and algebra
A revised relational structure is presented and the extended algebra is shown to be closed, a consistent extension of the conventional relational algebra, and reducible to the latter.
A review of uncertainty handling formalisms
This paper reviews some of the most important formalisms for dealing with incomplete and uncertain information, describing how they work, and in what ways they differ from one another.
A Bibliography on Uncertainty Management in Information Systems
  • C. Dyreson
  • Computer Science
    Uncertainty Management in Information Systems
  • 1996
This is an evolving bibliography of documents on uncertainty and imprecision in information systems, focusing almost exclusively on database and knowledge-base systems, with few references on other kinds of information systems.
The Management of Probabilistic Data
A data model that includes probabilities associated with the values of the attributes, and the notion of missing probabilities is introduced for partially specified probability distributions, offers a richer descriptive language allowing the database to more accurately reflect the uncertain real world.
Addendum to "Current Approaches to Handling Imperfect Information in Data and Knowledge Bases"
  • S. Parsons
  • Computer Science, Economics
    IEEE Trans. Knowl. Data Eng.
  • 1998
An attempt is made to classify the different types of imperfection that may pervade data, and a discussion of the sources of such imperfections are discussed.
Uncertainty Management in Information Systems: From Needs to Solution
This book presents viewpoints of information systems experts on the needs that challenge the uncertainty capabilities of present information systems, and it provides a forum to researchers in uncertainty modeling to describe models and systems that can address these needs.
A Mathematical Theory of Evidence
  • G. Shafer
  • Mathematics
    A Mathematical Theory of Evidence
  • 2020
This book develops an alternative to the additive set functions and the rule of conditioning of the Bayesian theory: set functions that need only be what Choquet called "monotone of order of infinity." and Dempster's rule for combining such set functions.