Solutions to the Identified Problems on Web Based Management System for Integrated Multi-located Supermarket

Solutions to the Identified Problems on Web Based Management System for Integrated Multi-located Supermarket

Ibegbu (2013), an enterprise database management system (DBMS) is a system used by companies to manage databases. It helps a company increase efficiency and is useful for companies with a large number of computer users needing access to information. Enterprises use DBMSs to plan and standardize their practices to increase overall efficiency in the company. They also help enterprises lower their costs. Databases must be managed efficiently and thoroughly to promote effectiveness in an organization. In general, the proposed system will provide solutions to the identified problems in the following ways.
• Storage
The new system will use a centralized database to tackle the storage problem. The sales data will be well stored and protected for future. The computer system memory will be large. There will also be extended and expanded memory facilities. In the event of increase in the volume of information, the system can always be upgraded to take care of the in.

• Information Referencing
This system presents an overall reference model for data management. This includes the identification of supporting layers, strategies for migration, transport, and reference data, and the four data standards. There are finally three dimensions for data to wisdom, data interoperability maturity, and interlocking data communities.
• Support
This project work sets out the requirements for data management program components, costing, and schedule. It lists the requirements for the various components, describes the operational environment, and sets out the needs for software, evolution,maintenance, technical support, and training.
• Overall Efficiency
The new system will be more efficient in terms of inputs, process and output of information.Information will be accurate, complete, clear concise and legible. There will be cost effective production. The integrated database system will operate at optimal efficiency.

According to Bruce (2009) generally, the databases to be integrated have to be developed independently and are heterogeneous in several respects. A worthwhile first step is therefore to attempt to reduce or eliminate such discrepancies. The path from heterogeneity to homogeneity may take three complementary routes:
• Syntactic Rewriting
The most visible heterogeneity is when existing databases have been installed on DBMSs based on different data models (relational, CODASYL, object-oriented). Efficient interoperation calls for the adoption of a common data model serving as information exchange standard among participating locations. Dedicated wrappers have to be developed to enforce data model transformations between the local model and the common model (Hammer 1997).
• Semantic Enrichment
Data model heterogeneity also induces semantic heterogeneity, in the sense that constructs in one model may provide a more accurate description of data than constructs in another model. For instance, an entity-relationship schema has different constructs for entities and associations, while some equivalent relational schema may describe the same data without making an explicit distinction between entities and associations. To compare the two schemas, one should be able to identify, in the relational schema, which relations describe entities and which relations describe associations. This is a veryprimitive form of semantic enrichment, i.e. the process that aims at augmenting theknowledge about the semantics of data.
• Representational Normalization
One more cause of heterogeneity is the non-determinism of the modeling process. Two designers representing the same real world situation with the same data model will inevitably end up with two different schemas. Enforcing modeling rules will reduce the heterogeneity of representations. This is referred to as representational normalization.

Wallace (1999) issues with combining heterogeneous data sources under a single query interface have existed for some time. The rapid adoption of databases after the 1960s naturally led to the need to share or to merge existing repositories. This merging can take place at several levels in the database architecture.One popular solution is implemented based on data warehousing. The warehouse system extracts, transforms, and loads data from heterogeneous sources into a single view schema so data becomes compatible with each other. This approach offers a tightly coupled architecture because the data are already physically reconciled in a single queriable repository, so it usually takes little time to resolve queries. However, problems lie in the data freshness, that is, information in warehouse is not always up-to-date. Thus updating an original data source may outdate the warehouse, accordingly, the ETL process needs re-execution for synchronization. Difficulties also arise in constructing data warehouses when one has only a query interface to summary data sources and no access to the full data. This problem frequently emerges when integrating several commercial query services like travel or classified advertisement web applications.
Bruno (2012) as of 2009 the trend in data integration has favored loosening the coupling between data and providing a unified query-interface to access real time data over a mediated schema, which allows information to be retrieved directly from original databases. This approach relies on a mappings between the mediated schema and the schema of original sources, and transform a query into specialized queries to match the schema of the original databases.

Christabel (2007) DBMSs are the technology tools that directly support managing organizational data. With a DBMS you can create a database including its logical structure and constraints, you can manipulate the data and information it contains, or you can directly create a simple database application or reporting tool. Human administrators, through a user interface, perform certain tasks with the tool such as creating a database, converting an existing database, or archiving a large and growing database. Business applications, which perform the higher level tasks of managing business processes, interact with end users and other applications and, to store and manage data, rely on and directly operate their own underlying database through a standard programming interface like ODBC.

• Database Engine:
The Database Engine is the core service for storing, processing, and securing data. The Database Engine provides controlled access and rapid transaction processing to meet the requirements of the most demanding data consuming applications within your enterprise. Use the Database Engine to create relational databases for online transaction processing or online analytical processing data. This includes creating tables for storing data, and database objects such as indexes, views, and stored procedures for viewing, managing, and securing data. You can use SQL Server Management Studio to manage the database objects, and SQL Server Profiler for capturing server events (Christabel, 2007).

• Data dictionary
A data dictionary is a reserved space within a database which is used to store information about the database itself. A data dictionary is a set of table and views which can only be read and never altered. Most data dictionaries contain different information about the data used in the enterprise. In terms of the database representation of the data, the data table defines all schema objects including views, tables, clusters, indexes, sequences, synonyms, procedures, packages, functions, triggers and many more. This will ensure that all these things follow one standard defined in the dictionary. The data dictionary also defines how much space has been allocated for and / or currently in used by all the schema objects.A data dictionary is used when finding information about users, objects, schema and storage structures. Every time a data definition language (DDL) statement is issued, the data dictionary becomes modified (Christabel, 2007).

• Query Processor
A relational database consists of many parts, but at its heart are two major components: the storage engine and the query processor. The storage engine writes data to and reads data from the disk. It manages records,
controls concurrency, and maintains log files.The query processor accepts SQL syntax, selects a plan for executing the syntax, and then executes the chosen plan. The user or program interacts with the query processor, and the query processor in turn interacts with the storage engine. The query processor isolates the user from the details of execution: The user specifies the result, and the query processor determines how this result is obtained.

• Report writer
Also called a report generator, a program, usually part of a database management system, that extracts information from one or more files and presents the information in a specified format. Most report writers allow you to select records that meet certain conditions and to display selected fields in rows and columns. You can also format data into pie charts, bar charts, and other diagrams. Once you have created a format for a report, you can save the format specifications in a file and continue reusing it for new data.

According to Anthony (2010) there are four different types of database users.

• Application programmers
A person who prepares application program are called application programmer. Application programs operates on the data in all the usual ways: retrieving information, creating new information, deleting or changing existing information.

• Sophisticated users
Sophisticated users interact with the system without writing programs. Instead, they form their requests in a database query language. Each such query is submitted to a query processor whose function is to take a DML statement and break it down into instructions that the database manager understands.

• Specialized users
Some sophisticated users write specialized database application that do not fit into the traditional data processing framework. Among these application are computer-aided design systems, knowledgebase and expert systems, systems that store data with complex data types eg:-For Graphics and Audio data.
• End users
Unsophisticated users interact with the system by invoking one of the permanent application programs that have been written previously. Thus they are persons who uses the information generated by a computer based system. Retrival is the most common function for this class of user.
• Naive users
They are unsophisticated users who interact with the system by using permanent application programs (e.g. automated teller machine).

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