Abstract: A method for partitioning a plurality of entities each associated with a plurality of ordered sequences of events received by a computer system, the method including: defining a minimal directed acyclic graph data structure representing the sequences of events to define a plurality of categories of behavior of the entities; defining a threshold degree of similarity as an xmu number, the xmu number having cardinality that is able to vary across a normalized range; defining a relation for each entity including a degree of association of the entity with each of the categories; defining a cluster of entities as a set of entities comprising a first entity; comparing a relation for the first entity with a relation for a second entity to define a xmu Jaccard similarity coefficient for the first and second entities; and responsive to the coefficient meeting the threshold degree of similarity, adding the second entity to the cluster.

Abstract: A defuzzification apparatus for defuzzification of fuzzy data representative of one or more entities or events, the fuzzy data specifying for each of the one or more entities or events, a membership level, the defuzzification apparatus including one or more processors configured to generate, using the fuzzy data, a set of computer executable instructions representative of a function of the fuzzy data, and execute, using a predetermined fuzzy membership value, the set of computer executable instructions to determine crisp data. The defuzzification apparatus may be coupled to crisp data processors configured to receive the crisp data and perform crisp data processing on the received crisp data so as to generate further crisp data. The crisp data processor may be coupled to a fuzzification apparatus including one or more processors configured to process the received further crisp data to generate therefrom further fuzzy data.

Abstract: A method for partitioning a plurality of entities each associated with a plurality of ordered sequences of events received by a computer system, the method including: defining a minimal directed acyclic graph data structure representing the sequences of events to define a plurality of categories of behavior of the entities; defining a threshold degree of similarity as an xmu number, the xmu number having cardinality that is able to vary across a normalized range; defining a relation for each entity including a degree of association of the entity with each of the categories; defining a cluster of entities as a set of entities comprising a first entity; comparing a relation for the first entity with a relation for a second entity to define a xmu Jaccard similarity coefficient for the first and second entities; and responsive to the coefficient meeting the threshold degree of similarity, adding the second entity to the cluster.

Abstract: Data analysis methods are characterized in terms of tangible and non-tangible parameters such as user-friendliness, accuracy, simplicity, interpretability, speed, cost, comprehensibility, and transparency, and then matched to pre-specified requirements. A data analysis method is selected in accordance with a user preference, wherein the user preference relates to a feature of the data analysis method and is represented by a fuzzy set comprising a range of values.

Abstract: A data repository stores data items with associated metadata values (21, 22 . . . 27), together with associated relatedness values (212, 217, 227) etc, defined between each pair of metadata values. In order to retrieve data, a ‘most relevant’ metadata value (21) is identified and data items associated with that metadata value are retrieved first. Other data items are ranked according to the relatedness value (217) of their associated metadata value (27) to the selected metadata value (21).

Abstract: Communications received by a user over a communications link include identification information and are processed by: extracting identification information associated with a first received communication; categorizing the first communication and recommending an action based on the categorization; monitoring user response to the first communication; modifying the categorization in accordance with a monitored user response to the first communication, such that when a second communication having identifying information matching that of the first communication is received, the monitored user response is recommended by the categorization.

Abstract: With known systems, when performing analysis of data, users typically have a solution-oriented view: there is a problem, and data has been collected with a view to providing some information about the problem. Usually a method is selected, based on vague knowledge about some feature of the problem. This is often sub-optimal, because the user can be assumed to be an expert in the application area, but not in data analysis. Embodiments of the invention provide a means of characterising data analysis methods in terms of tangible and non-tangible parameters such as user-friendliness, accuracy, simplicity, interpretability, speed, cost, comprehensibility, and transparency, and then matching these to some pre-specified requirements. This means is provided by a method of selecting a data analysis method in accordance with a user preference, wherein the user preference relates to a feature of the data analysis method and is represented by a fuzzy set comprising a range of values.