arXiv subjects

Neha Rino

Publications and source records attributed to Neha Rino.

4 recordsLinked to original sources

arXiv2026

Algorithms and fine-grained complexity for nondeterministic and symmetric difference automata

Symmetric difference automata (XNFA) are a variant of standard finite automata in which an input word is accepted iff the number of accepting runs is odd. Equivalently, these are weighted automata over the two-element field. We study the fine-grained complexity of the basic decision problems for XNFA: acceptance, emptiness, and equivalence, aiming to optimise the degree of the polynomial in their running-time bounds. Under the assumption of polynomial ambiguity, we provide a randomised reduction of NFA acceptance to XNFA acceptance. For automata of bounded ambiguity (e.g., unambiguous automata), we show that acceptance for both NFA and XNFA can be decided faster than in the general case. Without ambiguity assumptions, we give faster algorithms for the verification of suitable certificates for (non)emptiness and (non)equivalence of XNFA. Several of our results extend to weighted automata over other semirings and fields.

cs.FL↗

arXiv2026

Intersecting Dense Automata

We observe that the classical Cartesian product construction for the intersection of (languages of) nondeterministic finite automata (NFA) is non-optimal in the worst case, if the automata have many transitions. For a fixed alphabet, the product of two NFA may have $\Theta(m^2)$ transitions if these NFA have at most $n$ states and $m$ transitions each. We describe alternative constructions with $O(m n)$ transitions: or $O(m n^{k-1})$ for the intersection of $k$ NFA (for fixed $k \ge 2$ and alphabet $\Sigma$). This gives a faster algorithm for deciding NFA intersection emptiness. The new algorithm is optimal, unless there exists a breakthrough combinatorial algorithm for detecting $(k+1)$-cliques in undirected graphs. This also leads to a more efficient certification scheme for NFA intersection emptiness.

cs.FL↗

arXiv2022

Timed Alignments with Mixed Moves

The subject of this paper is to study conformance checking for timed models, that is, process models that consider both the sequence of events in a process as well as the timestamps at which each event is recorded. Time-aware process mining is a growing subfield of research, and as tools that seek to discover timing related properties in processes develop, so does the need for conformance checking techniques that can tackle time constraints and provide insightful quality measures for time-aware process models. In particular, one of the most useful conformance artefacts is the alignment, that is, finding the minimal changes necessary to correct a new observation to conform to a process model. This paper follows a previous one, where we have set our problem of timed alignment. In the present paper, we solve the case where the metrics used to compare timed processes allows mixed moves, i.e. an error on the timestamp of an event may or may not have propagated to its successors, and provide linear time algorithms for distance computation and alignment on models with sequential causal processes.

cs.FL↗

arXiv2022

Timed Alignments

The subject of this paper is to study conformance checking for timed models, that is, process models that consider both the sequence of events in a process as well as the timestamps at which each event is recorded. Time-aware process mining is a growing subfield of research, and as tools that seek to discover timing related properties in processes develop, so does the need for conformance checking techniques that can tackle time constraints and provide insightful quality measures for time-aware process models. In particular, one of the most useful conformance artefacts is the alignment, that is, finding the minimal changes necessary to correct a new observation to conform to a process model. In this paper, we set our problem of timed alignment and solve two cases each corresponding to a different metric over time processes. For the first, we have an algorithm whose time complexity is linear both in the size of the observed trace and the process model, while for the second we have a quadratic time algorithm for linear process models.

cs.FL↗