A prototype database of mineral information of minfo-Iron ore resources in India

Data | database
A prototype database of mineral information of minfo-Iron ore resources in India

Minfo--a protype Mineral Information database for iron ore resources of India

(Computers & Geosciences 27 (2001) 357-361)

Author: Indranil ROY,B.C Sarker, A. Chattopadhyay

Translation: Yang Heohong

Introduced

The growing demand for mineral resources brings the same large demand for resource-related information. Detection and related mining activities produced a large amount of data. In India, because of the lack of communication and the absence of a central data warehouse, this information remains in the original data state. This problem can be solved by establishing a computerized mineral information database. India's rich iron ore resources are mainly distributed in 5 districts, namely area A (South ratio Hal and northern Orissa), Zone B (baster, Rajhara, rowghat area of Madhya Pradesh), Area C (Bellary, Karnataka, Hospet), Area d (Goa) and E (Bababudan, Kudremukh Region of Karnataka) (Fig 1). (Banerjee and sharma,1994). Currently, there are 259 operating iron mines and hundreds of untapped occurrences in India (deposit) (Indian Mining Bureau, 1992). In this situation, in accordance with India's new national mining policy, the Indian iron Ore resources mineral Information Database was attempted to develop, MINFO. To date, the prototype system includes information on 32 iron ore points in area A. The information on these occurrences is collected from various published documents and various mining reports. The forms used for information processing at various occurrences are ready. In order to ensure reliability, visits to various occurrences of repeated checksum information. In addition, data files are made for each occurrences and linked to the system separately. This article describes the-minfo of the mineral information prototype database for the development of Indian mineral resources. It mainly emphasizes the construction of MINFO database and information management system, including the structure of data file and user interface.

System Requirements

The MINFO database is developed using Turbo PASCAL (ver.6.0), which can be run in DOS3.0 or later, in any IBM PC compatible machine, preferably 486 or higher microprocessors and a minimum of 2.46kB RAM. The core module requires 2.46MB of hard disk space. The storage space in the database section depends on the size of the database, and the data for each occurrence requires 1.84kB.

The variable organization of the database

According to Clark and Cook (1978), in order to fully define an occurrence, 7 types of information must be considered. including (i) Cataloging and information about the overall organization, (ii) Geographical location information of occurrences, (iii) leasing information and other regulatory aspects, (iv) Description of existing mineralogy, minerals and mineral processing, (v) various types of reserves information, (vi) Geological information of occurrences, and (vii) information on current mining activities. In the Minfo mineral information database, these 7 kinds of information are subdivided into 64 domains. The various category information and related fields are shown in table 1.

Database constructs and files

Minfo Mineral Information Database consists of a core information management program file, 3 system files (config.mnf,fields. MNF and help. MNF) and 2 types of data files (that is, master record files and information files for individual occurrences). System files and master record files (master data files) are linked to the core program files on the system structure. On the other hand, data files for each occurrence information are linked to a mixed structure of the master record file. The entire database structure is shown in Figure 2. The master record portion of the database that is the catalog of occurrences is a natural table. Each record in the table represents an occurrence and links to separate files that contain information about the corresponding occurrences. Each of these files is small (184kB), allowing for quick access and optimal performance in limited storage space on a PC running DOS. The record in the center of the main table contains the name of the occurrence, the detail record file name, the occurrences code, the creation date, the creator name, the last modification date, the modified person name, and the presence or absence of any category details representing the occurrences. These tags increase the speed of the query. Adding or removing any occurrences is reflected on the master record. level, the structured information file for each occurrence includes the title, the name of the occurrence, and the unique occurrences code, as well as the various category fields that describe the information of the occurrences, as shown in table 1. The system file FIELDS.MNF lists the individual data fields and their hierarchical relationships. For structured user-defined queries. While the primary field is predefined during the query, virtually any combination of the different fields can be used to create the final search variable. This makes it much more flexible to construct the scope of the query. Practical user-defined query, the system in 2 phases to search the database. First of all, by testing the mineral information category of the tag linear search master record file, create the name list of occurrences, that is, the query requested occurrences. Occurrences marked with a negative value are excluded from the list. Then, the master record file is used as a multilink node (that is, a pointer to a file), and the data file for each occurrence name in the list is accessed and tested by a query variable. Finally, create a new list to store the results.

User interface

The Minfo database user interface is a multi-level menu based on a few hotkeys (for special purposes, as shown in Figure 3). Among the many options in the main menu (Figure 3), the view option allows the user to browse the information stored in the database (Figure 4). The edit option is used to update and correct errors in data items. The new record (add new) and add data (add) are used to add new occurrences records and information categories to the database, respectively. In this process, a new data file is created and the header information is inserted into the occurrences of the main record file. The delete option, on the other hand, is used to delete a record from the database that corresponds to a selected occurrence, including physical deletion of the data file and deletion of records in the master record file. Utility report option to print stored information or export an ASCII text file from a database. The query process of Minfo mineral information database is divided into 2 stages. Users first use a series of related menu structure formulations of the expression of a query. Each search variable consists of a number or string specified by the user, with a logical operator () that makes up the expression of the connection to a particular data field. These search variables can also be further concatenated with Boolean operators (And,or and not). Continuous system reflection () helps users create queries that are very close to natural language. After the parsed query executes, the user is free to choose to export the results to the screen (default), printer or file.

Conclusion

Given India's extensive iron ore resources, the MINFO database provides a quick and effective way to store, search, and extract specific data, minimizing redundancy. The release of well-organized, specific information, and the description and quantification of resources for future resource assessment, planning and development, while planning and revising policies, will play a role. The system is not entirely in place, and modules on environmental parameters and mining infrastructure are being integrated. For further development, the system maintains full openness to the future combined with other products. By leveraging configuration options, the Minfo mineral Information Database engine can be customized to work with any dataset, so it can be further developed into a broader mineral product information system.

Thank

The first author thanked Csir for his financial support for the research Project Grant NO 9/85/(/96/emr-1). The second and third authors thanked AICTE for the research project Grant No. Tmat 020/rec 387 of financial support. Also thanks to the anonymous commentator who provided constructive advice.

Reference documents

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