A New Top-Down Context-Free Parsing for Syntactic Pattern Recognition

The numerous different mathematical methods used to solve pattern recognition snags may be assembled into two universal approaches: the decision-theoretic approach and the syntactic(structural) approach. In this paper, at first syntactic pattern recognition method and formal grammars are described and then has been investigated one of the techniques in syntactic pattern recognition called top – down tabular parser known as Earley’s algorithm Earley's tabular parser is one of the methods of context -free grammar parsing for syntactic pattern recognition. Earley's algorithm uses array data structure for implementing, which is the main problem and for this reason takes a lots of time, searching in array and grammar parsing, and wasting lots of memory. In order to solve these problems and most important, the cubic time complexity, in this article, a new algorithm has been introduced, which reduces wasting the memory to zero, with using linked list data structure. Also, with the changes in the implementation and performance of the algorithm, cubic time complexity has transformed into O (n*R) order. Key words: syntactic pattern recognition, tabular parser, context –free grammar, time complexity, linked list data structure.

On the Use of Model Checking for the Bounded and Unbounded Verification of Nonblocking Concurrent Data Structures

<p>Concurrent data structure algorithms have traditionally been designed using locks to regulate the behaviour of interacting threads, thus restricting access to parts of the shared memory to only one thread at a time. Since locks can lead to issues of performance and scalability, there has been interest in designing so-called nonblocking algorithms that do not use locks. However, designing and reasoning about concurrent systems is difficult, and is even more so for nonblocking systems, as evidenced by the number of incorrect algorithms in the literature. This thesis explores how the technique of model checking can aid the testing and verification of nonblocking data structure algorithms. Model checking is an automated verification method for finite state systems, and is able to produce counterexamples when verification fails. For verification, concurrent data structures are considered to be infinite state systems, as there is no bound on the number of interacting threads, the number of elements in the data structure, nor the number of possible distinct data values. Thus, in order to analyse concurrent data structures with model checking, we must either place finite bounds upon them, or employ an abstraction technique that will construct a finite system with the same properties. First, we discuss how nonblocking data structures can be best represented for model checking, and how to specify the properties we are interested in verifying. These properties are the safety property linearisability, and the progress properties wait-freedom, lock-freedom and obstructionfreedom. Second, we investigate using model checking for exhaustive testing, by verifying bounded (and hence finite state) instances of nonblocking data structures, parameterised by the number of threads, the number of distinct data values, and the size of storage memory (e.g. array length, or maximum number of linked list nodes). It is widely held, based on anecdotal evidence, that most bugs occur in small instances. We investigate the smallest bounds needed to falsify a number of incorrect algorithms, which supports this hypothesis. We also investigate verifying a number of correct algorithms for a range of bounds. If an algorithm can be verified for bounds significantly higher than the minimum bounds needed for falsification, then we argue it provides a high degree of confidence in the general correctness of the algorithm. However, with the available hardware we were not able to verify any of the algorithms to high enough bounds to claim such confidence. Third, we investigate using model checking to verify nonblocking data structures by employing the technique of canonical abstraction to construct finite state representations of the unbounded algorithms. Canonical abstraction represents abstract states as 3-valued logical structures, and allows the initial coarse abstraction to be refined as necessary by adding derived predicates. We introduce several novel derived predicates and show how these allow linearisability to be verified for linked list based nonblocking stack and queue algorithms. This is achieved within the standard canonical abstraction framework, in contrast to recent approaches that have added extra abstraction techniques on top to achieve the same goal. The finite state systems we construct using canonical abstraction are still relatively large, being exponential in the number of distinct abstract thread objects. We present an alternative application of canonical abstraction, which more coarsely collapses all threads in a state to be represented by a single abstract thread object. In addition, we define further novel derived predicates, and show that these allow linearisability to be verified for the same stack and queue algorithms far more efficiently.</p>

On the Use of Model Checking for the Bounded and Unbounded Verification of Nonblocking Concurrent Data Structures

<p>Concurrent data structure algorithms have traditionally been designed using locks to regulate the behaviour of interacting threads, thus restricting access to parts of the shared memory to only one thread at a time. Since locks can lead to issues of performance and scalability, there has been interest in designing so-called nonblocking algorithms that do not use locks. However, designing and reasoning about concurrent systems is difficult, and is even more so for nonblocking systems, as evidenced by the number of incorrect algorithms in the literature. This thesis explores how the technique of model checking can aid the testing and verification of nonblocking data structure algorithms. Model checking is an automated verification method for finite state systems, and is able to produce counterexamples when verification fails. For verification, concurrent data structures are considered to be infinite state systems, as there is no bound on the number of interacting threads, the number of elements in the data structure, nor the number of possible distinct data values. Thus, in order to analyse concurrent data structures with model checking, we must either place finite bounds upon them, or employ an abstraction technique that will construct a finite system with the same properties. First, we discuss how nonblocking data structures can be best represented for model checking, and how to specify the properties we are interested in verifying. These properties are the safety property linearisability, and the progress properties wait-freedom, lock-freedom and obstructionfreedom. Second, we investigate using model checking for exhaustive testing, by verifying bounded (and hence finite state) instances of nonblocking data structures, parameterised by the number of threads, the number of distinct data values, and the size of storage memory (e.g. array length, or maximum number of linked list nodes). It is widely held, based on anecdotal evidence, that most bugs occur in small instances. We investigate the smallest bounds needed to falsify a number of incorrect algorithms, which supports this hypothesis. We also investigate verifying a number of correct algorithms for a range of bounds. If an algorithm can be verified for bounds significantly higher than the minimum bounds needed for falsification, then we argue it provides a high degree of confidence in the general correctness of the algorithm. However, with the available hardware we were not able to verify any of the algorithms to high enough bounds to claim such confidence. Third, we investigate using model checking to verify nonblocking data structures by employing the technique of canonical abstraction to construct finite state representations of the unbounded algorithms. Canonical abstraction represents abstract states as 3-valued logical structures, and allows the initial coarse abstraction to be refined as necessary by adding derived predicates. We introduce several novel derived predicates and show how these allow linearisability to be verified for linked list based nonblocking stack and queue algorithms. This is achieved within the standard canonical abstraction framework, in contrast to recent approaches that have added extra abstraction techniques on top to achieve the same goal. The finite state systems we construct using canonical abstraction are still relatively large, being exponential in the number of distinct abstract thread objects. We present an alternative application of canonical abstraction, which more coarsely collapses all threads in a state to be represented by a single abstract thread object. In addition, we define further novel derived predicates, and show that these allow linearisability to be verified for the same stack and queue algorithms far more efficiently.</p>

Sequential linked data: The state of affairs

Sequences are among the most important data structures in computer science. In the Semantic Web, however, little attention has been given to Sequential Linked Data. In previous work, we have discussed the data models that Knowledge Graphs commonly use for representing sequences and showed how these models have an impact on query performance and that this impact is invariant to triplestore implementations. However, the specific list operations that the management of Sequential Linked Data requires beyond the simple retrieval of an entire list or a range of its elements – e.g. to add or remove elements from a list –, and their impact in the various list data models, remain unclear. Covering this knowledge gap would be a significant step towards the realization of a Semantic Web list Application Programming Interface (API) that standardizes list manipulation and generalizes beyond specific data models. In order to address these challenges towards the realization of such an API, we build on our previous work in understanding the effects of various sequential data models for Knowledge Graphs, extending our benchmark and proposing a set of read-write Semantic Web list operations in SPARQL, with insert, update and delete support. To do so, we identify five classic list-based computer science sequential data structures (linked list, double linked list, stack, queue, and array), from which we derive nine atomic read-write operations for Semantic Web lists. We propose a SPARQL implementation of these operations with five typical RDF data models and compare their performance by executing them against six increasing dataset sizes and four different triplestores. In light of our results, we discuss the feasibility of our devised API and reflect on the state of affairs of Sequential Linked Data.

Parallel Merging and Sorting on Linked List

We study linked list sorting and merging on the PRAM model. In this paper we show that n real numbers can be sorted into a linked list in constant time with n2+e processors or in ) time with n2 processors. We also show that two sorted linked lists of n integers in {0, 1, …, m} can be merged into one sorted linked list in O(log(c)n(loglogm)1/2) time using n/(log(c)n(loglogm)1/2) processors, where c is an arbitrarily large constant.