Specification Mining over Temporal Data

Abstract

Current specification mining algorithms for temporal data rely on exhaustive search approaches, which become detrimental in real data settings where a plethora of distinct temporal behaviours are recorded over prolonged observations. This paper proposes a novel algorithm, Bolt2, based on a refined heuristic search of our previous algorithm, Bolt. Our experiments show that the proposed approach not only surpasses exhaustive search methods in terms of running time but also guarantees a minimal description that captures the overall temporal behaviour. This is achieved through a hypothesis lattice search that exploits support metrics. Our novel specification mining algorithm also outperforms the results achieved in our previous contribution.

Streamlining Temporal Formal Verification over Columnar Databases

“…To support our research claims, we extend (https://github.com/datagram-db/knobab/ releases/tag/v2.3, accessed on 3 January 2024) the current implementation of KnoBAB [34], a column-oriented main memory DBMS supporting formal verification and specification mining tasks by defining relational operations for temporal logic and customary mining algorithms. Despite this being a main memory engine, it currently supports intra-query parallelism and hybrid algorithms (Section 2.2.1).…”

“…KnoBAB [28,34] is a column database store tailored for both loading dataful logs being represented in XES [38] and dataless ones described as a tab-separated file. This outper-formed the previous state of the art in terms of both specification mining [39] and formal verification [35] tasks on tailored non-database solutions.…”

DECLARE<em>d</em>: A Polytime LTL_f Fragment

“…AALTAF 4 [15] is a SAT-checker determining whether an LTL f formula is satisfiable by generating a corresponding transition system where each state represents subformulae of the original LTL f specification while leveraging traditional SAT-solvers. Differently from KnoBAB, which determines whether traces in a log satisfy an LTL f specification expressed in algebraic terms (xtLTL f ) or not, AALTAF is more general than this and determines whether no traces will ever satisfy a given specification, thus determining its unsatisfiability, or whether there might exist a finite trace allowing this.…”

“…Verified Artificial Intelligence [1] calls for exact procedures ascertaining whether a model of the system S abides by the specifications in Φ through yes or no answers (S ⊨ Φ) when written in a formalism for efficient computations, either for verifying the compliance of a system to a specification (formal verification [2]) or for producing a system abiding by a given specification (formal synthesis [3]). This can be determined after a specification mining phase used to extract Φ from a system S [4]; these considerations bridge temporal reasoning with artificial intelligence, as in both we can extract a specification from the data that can be used to determine decision problems. Under these assumptions, we are then interested in a temporal description of such systems, when different runs are collected as logs S and referred to as traces σ ∈ S. These are temporally ordered records of observed and completed (or aborted) labelled activities.…”

“…2 of 30 the abidance by the hospitalization procedures [8]. These specifications do not necessarily have to be hard-coded, but can be mined from such logs [4]. For BPM, each event is associated with exactly one single label [9] and, under the occasion that each event is also dataful and therefore associated with some data payload, we can always generate a finite set of mutually exclusive atoms partitioning the data space into non-overlapping intervals [10].…”