Deterministic Finite Automation Research Articles

A Brief Survey and Implementation on Refinement for Intent-Driven Networking

Intent-driven networking (IDN) is emerging because the traditional network is complicated and error-prone when configured. As the key step of IDN, intent refinement is an alternative approach to convert intents from declarative language to a machine-readable policy, which possesses important scientific significance and application value. In this article, we first present a generic architecture to illustrate the process and functions of IDN. Then we give a definition of intent refinement to make the concept clear. In order to propose a standard classification method of intent refinement, we review the typical intent refinement schemes and distinguish them according to target users, input methods, and refinement approaches. Finally, in order to refine different kinds of intent, we design an intelligent intent refinement system based on natural language processing and deterministic finite automation. In summary, intent refinement plays a crucial role in IDN, which provides a convenient northbound interface for different users to express their communication requirements. Therefore, it is of vital importance to summarize its common methods and explore more straightforward and intelligent ways to enhance its utility.

ATrie Group Join: A Parallel Star Group Join and Aggregation for In-Memory Column-Stores

This article presents a new holistic and efficient approach to big data analysis. We introduce a new parallel algorithm, known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATrie Group Join (ATGJ)</i> , that integrates join, grouping and aggregation operations to accelerate big data analytical workloads in in-memory column-stores. ATGJ performs a single scan of the fact columns and uses a mixture of data and task parallelism for the optimal use of computing resources. It uses a novel technique to perform group-by and aggregation realising the grouping attributes as a tree shaped deterministic finite automation known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Aggregate Trie</i> or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATrie</i> . ATrie facilitates grouping of attributes and processing of data in tight loops that significantly improves the performance on modern hardware. Unlike other competing algorithms, use of ATrie avoids the creation of multiple data structures with the increasing number of dimension tables and grouping attributes. Also, we demonstrate that ATGJ performs efficiently even when the ATrie becomes bushy. We evaluated the algorithm using Star Schema Benchmark (SSBM) to show that it is significantly faster and scales better than other algorithms for the number of concurrent threads, the number of group-by attributes, the data set size and the query complexity.

Distributed ATrie Group Join: Towards Zero Network Cost

The combination of powerful parallel frameworks and on-demand commodity hardware in distributed computing has made both analytics and decision support systems canonical to enterprises of all sizes. The unprecedented volumes of data stacked by companies present challenges to process analytical queries efficiently. This data is often organised as star schema, in which star join and group-by are ubiquitous and expensive operations. Although parallel frameworks such as Apache Spark facilitate join and group-by, the implementation can only process two tables at a time and fail to handle the excessive network communication, disk spills and multiple scans of data. In this paper, we present Distributed ATrie Group Join (DATGJ), a fast distributed star join and group-by algorithm for column-stores. DATGJ uses divide and broadcast-based joining technique where the fact table columns are partitioned equally and fast hash table (FHT) for each dimension table are broadcasted. This technique helps it avoid cross communication between workers and disk spills. DATGJ performs a single scan of partitioned fact table columns and use FHT to join data. FHT uses Robin Hood hashing with the upper limit on number of probes and achieve significant speed up during join. DATGJ performs group-by and aggregation leveraging progressive materialisation and realising grouping attributes as a tree shaped deterministic finite automation known as Aggregate Trie or ATrie. We evaluated our algorithm using Star Schema Benchmark (SSBM) to show that it is 1.5X to 6X faster than the most prominent approaches while having zero data shuffle and consistently perform well with addition of resources and in memory-constrained scenarios.

Effective learning in recurrent max–min neural networks

Max and min operations have interesting properties that facilitate the exchange of information between the symbolic and real-valued domains. As such, neural networks that employ max–min activation functions have been a subject of interest in recent years. Since max–min functions are not strictly differentiable, we propose a mathematically sound learning method based on using Fourier convergence analysis of side-derivatives to derive a gradient descent technique for max–min error functions. We then propose a novel recurrent max–min neural network model that is trained to perform grammatical inference as an application example. Comparisons made between this model and recurrent sigmoidal neural networks show that our model not only performs better in terms of learning speed and generalization, but that its final weight configuration allows a deterministic finite automation (DFA) to be extracted in a straightforward manner. In essence, we are able to demonstrate that our proposed gradient descent technique does allow max–min neural networks to learn effectively.

A Process Algebra Foundation for Reasoning about Core ELLA

A process algebraic foundation has been developed for formal analysis of synchronous hardware designs represented through the commercially available hardware design language, ELLA. An underlying semantic foundation, based on input/output trace sets, is presented first through the use of state machines. Such a representation enables direct application of standard, fully automated trace equivalence checking tools. However, to overcome the computational limitations imposed by such analysis methods, the input/ output trace semantics is represented through a synchronous process algebra, EPA. Primitive processes in EPA denote the behaviour of primitive hardware components, such as delays or multiplexers, with composition operators corresponding to the different ways in which behaviours may be built. Of particular significance is the parallel composition operator which captures the machinery for building networks from other components/networks. Actions in EPA are structured and signify the state of input and output signals. This structure, however, is abstracted by developing an algebra for the actions. In particular, parallel composition on processes neatly lifts to a special (synchronous) product operation on actions. The EPA representation forms a good basis for semi-automated high-level symbolic manipulation and reasoning tools. First, the original design structure can be maintained, thus easing the problems of user level feedback from tools. Secondly, the application of EPA to ELLA enables a deterministic finite automation form for EPA terms. This provides a route to tractable symbolic verification and simulation, using a state evolution method to establish strong bisimulation properties. The method has been successfully applied to classes of unbounded state space systems.

A polynomial time algorithm for the local testability problem of deterministic finite automata

The local testability problem of deterministic finite automata is investigated. A locally testable language is a language with the property that, for some nonnegative integer k, whether or not a word w is in the language depends on (1) the prefix and suffix of w of length k, and (2) the set of substrings of w length k+1, without regard to the order in which these substrings occur. The local testability problem is, given a deterministic finite automation, to decide whether or not it accepts a locally testable language. The authors present an O(n/sup 2/) time algorithm for the local testability problem based on two simple properties that characterize locally testable automata.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A syntactic/geometric approach to recognition of dimensions in engineering machine drawings

An algorithm for recognizing dimensions in engineering machine drawings that employs a syntactic/geometric approach along with a specific deterministic finite automation (DFA) is presented and demonstrated. First, the problem of distinguishing object from interpretation lines is addressed. Then, dimension-sets, their components, kinds, and types are defined and illustrated. A DFA called the dimension-set profiler is used to determine the profile of a given dimension-set. The resulting profile is used to obtain a sketch which is parsed by the dimensioning grammar to yield a conceptual web. This web, in turn, is converted into a geometric web by substituting components labeling nodes by their line description. These line descriptions are compared to the lines in the actual dimension-set. A certain degree of redundancy is introduced to ascertain valid recognition.

Advances in binary decision based programmable controllers

Binary decision automata are finite state machines that evaluate switching functions by means of decision rather than Boolean logic. The capability of binary decision machines (BDMs) to evaluate sequential functions is addressed by the authors. The BDM is shown to be less powerful than the deterministic finite automation (DFA) model from automata theory. However, an extended BDM with input control is shown to be equivalent to the DFA and thus can be used to implement finitely computable sequential functions. The use of a BDM finite automaton instead of a more general model is motivated on the basis of expected case time and space complexity analysis. A hardware design following from this analysis is described, and programming methods are discussed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>