K. M. Azharul Hasan

An Implementation Scheme for Multidimensional Extendable Array Operations and Its Evaluation

In recent times, multidimensional arrays are extensively used to handle large amount of data in scientific research, engineering, and MOLAP applications. Most of the existing data structures are static in nature and cannot grow or shrink dynamically. The Traditional Multidimensional Array (TMA) is an efficient structure in terms of accessing the element of the array by straight computation of the addressing function, but TMA is not extendible during run time. There are some extendible data structures but they have the problem of address space overflow for large number of dimensions or length of dimension. In this paper we propose a new data structure, Extendible Karnaugh Array (EKA), to represent the multidimensional data. The main idea of this scheme is to represent n dimensional array by a set of two dimensional extendible arrays. The scheme can be extended in any direction during run time. We evaluate our proposed scheme by comparing for different operations with the Traditional Multidimensional Array (TMA). Our experimental result shows that the EKA scheme outperforms TMA.

A context free grammar and its predictive parser for bangla grammar recognition

Parsing is a process of transforming natural language into an internal system representation, which can be trees, dependency graphs, frames or some other structural representations. If a natural language be successfully parsed then grammar checking from this language becomes easy. In this paper we describe a context free grammar for Bangla language and hence we develop a Bangla parser based on the grammar. Our approach is very much general to apply in Bangla Sentences and the method is well accepted for parsing a language of a grammar. The scheme is based on Top down parsing method and to avoid the left recursion the idea of left factoring is adopted.

Recognizing Bangla Grammar using Predictive Parser

We describe a Context Free Grammar (CFG) for Bangla language and hence we propose a Bangla parser based on the grammar. Our approach is very much general to apply in Bangla Sentences and the method is well accepted for parsing a language of a grammar. The proposed parser is a predictive parser and we construct the parse table for recognizing Bangla grammar. Using the parse table we recognize syntactical mistakes of Bangla sentences when there is no entry for a terminal in the parse table. If a natural language can be successfully parsed then grammar checking from this language becomes possible. The proposed scheme is based on Top down parsing method and we have avoided the left recursion of the CFG using the idea of left factoring.

An extendible data structure for handling large multidimensional data sets

Multidimensional array is widely used in large number of scientific research and engineering applications for handling large multidimensional data. There exist many data structures to represent multidimensional data. But most of these data structures are static (such as traditional multidimensional array) and can not handle the dynamic extension or reduction of the array. The Traditional Multidimensional Array (TMA) is efficient in terms of accessing the elements of the array by random computing the addressing function but TMA is not extendible during run time. In this paper we propose a new scheme, Karnaugh Representation of Extendible Array (KEA), to represent the multidimensional data. The main idea of this scheme is to represent n dimensional array by a set of two dimensional extendible arrays. The scheme can be extended in any direction during run time. To evaluate our proposed scheme, we implement and compare with the existing systems for different operations with the Traditional Multidimensional Array (TMA), and Traditional Extendible Array (TEA). Our experimental result shows that the KEA scheme outperforms TMA and TEA.

An Efficient Encoding Scheme to Handle the Address Space Overflow for Large Multidimensional Arrays

We present a new implementation scheme of multidimensional array for handling large scale high dimensional datasets that grows incrementally. The scheme implements a dynamic multidimensional extendible array employing a set of two dimensional extendible arrays. The multidimensional arrays provide many advantages but it has some problems as well. The Traditional Multidimensional array is not dynamic extendible. Again, if the length of dimension and number of dimension of a multidimensional array is large then the address space for the array overflows quickly. In this paper, we propose a solution against the essential problem of address space overflow for handling large scale multidimensional datasets using our implementation model. We also propose a record encoding scheme based on our model for representing relational tables using multidimensional array. We evaluate our proposed scheme by comparing with Traditional Multidimensional Array (TMA) for different operations and find a reasonable delay of address space overflow with no retrieval penalty. We also compare the encoded scheme with traditional scheme and find that proposed encoded scheme performs better on range retrieval for sparse array.

EaCRS: an extendible array based compression scheme for high dimensional data

Multidimensional arrays are becoming important data structure for handling large scale multidimensional data; e.g., in scientific databases or MOLAP databases. Due to the increasing size of the data warehouses and high degree of sparsity, it becomes necessity to develop a suitable scheme to compress the multidimensional array in an efficient way so that it takes comparatively low memory storage. In this paper, we propose a new compression scheme namely extendible array based Compressed Row Storage (EaCRS), for large multidimensional sparse array. The main idea of this scheme is to compress the subarrays found from the existing extendible array using CRS method. To evaluate the proposed scheme, we compare it to the CRS on Traditional multidimensional array (TMA). Both analytical analysis and experimental test were conducted. In the analytical analysis, we analyze the CRS and EaCRS schemes in terms of the space requirement and the maximum range of usable data density for practical applications. The analytical analysis and experimental results show that the EaCRS scheme is superior to the CRS scheme for all the evaluated criteria.

Representing Higher Dimensional Arrays into Generalized Two-Dimensional Array: G2A

Two dimensional array operations are prominent for array applications because of their simplicity and good performance. But in practical applications, the array dimension is large and hence efficient design of multidimensional array operation is an important research issue. In this paper, we propose a two dimensional representation of multidimensional array. The scheme converts an n dimensional array into a two dimensional array. We design efficient algorithms for matrix-matrix addition/subtraction and multiplication using our scheme. The experimental results show that the proposed scheme outperforms the Traditional Multidimensional Array (TMA) based algorithms.

A rule based approach for NLP based query processing

Information is playing an important role in our lives. One of the major sources of information is databases. Databases and database technology are having major impact on the growing use of computers. In order to retrieve information from a database, one needs to formulate a query in such way that the computer will understand and produce the desired output. Generally, query processing is handled by the Structured Query Language (SQL). But the non IT people cannot be able to write SQL queries as they may not be aware of the SQL as well as structure of the database. So there is a need for non-expert users to query the databases in their natural language instead of working with the values of the attributes. This paper proposes an approach for accessing the database easily using natural language without having any knowledge about the query language. The approach is a rule based approach. The obvious advantage is that it makes a great promise for computer interfaces easier for the use of general people. Because of this, people will be able to communicate to the computer in their own language instead of learning a specialized language or commands. In order to test our approach in an actual computer environment, we have developed a prototype system. We obtained promising results using our system.

Opinion mining using Naïve Bayes

The opinion of other person is an important information for decision making. People share useful information to others as the growth of social media through Internet. This information is used to organize, explore and analyze for better decision making. Opinion Mining is a Natural Language Processing task which aims to determine the attitude of a person by identifying and extracting information. The major task in opinion mining is to classify the polarity of a review at sentence level, whether the expressed opinion is positive or negative. In this paper, we design a classifier using Naïve Bayes as a machine learning approach to determine the opinion expressed both in English and Bangla. We label the polarity of each opinion as weak, steady and strong. The performances are evaluated and the comparative results are analyzed.

Chunking implementation of extendible array to handle address space overflow for large multidimensional data sets

Multidimensional array is commonly used in data warehouse or in many scientific computation of OLAP for dealing with application dataset. Different models of multidimensional array have been introduced to do this job like Traditional Multidimensional Array, Extendible array, Extended Karnaugh Map Representation etc. But Application that requires operation on large amount of data cannot be handled by these models. One common approach is to store data in chunked multidimensional arrays. Indices into an array are obtained by a normalization process that maps attribute values to integers. We have introduced an approach of storing huge amount of data contiguously in disk by applying the chunking approach to an extendible multidimensional array. This implemented approach is effective in managing memory than any other existing approach such as traditional multidimensional array. In our system data are stored in memory according to some predefined amount of memory space that will help the memory to store large amount of data through small unit of chunk where the size of chunk will never overflow the address space limit of a computer.