Сенин Д.С., к.т.н., доц. Булатов Л.Н., к.ф.н., доц. Тюрина С.Ю.

Ивановский Государственный Энергетический Университет им В.И.Ленина

Development of corporate database to enhance studying process

The article discusses the issue of development software that is supposed to help to maintain, analyze and optimize a studying process and as a result increase its efficiency.

Nowadays many organizations tend to automate internal management processes using modern software. This allows to decrease effort of administration and collect huge amount of data about the organization in order to perform post-processing and analysis. In future, such approach may help to optimize business processes and as a result save money and other resources.

Studying may be considered a business process too. Therefore, in this sphere the same approaches of running and estimation may be applied. We consider the studying process as separate steps, which include preparing, maintenance and handling some particular documents. In Russia, the list of such documents includes a curriculum and the report with marks. In Ivanovo State Power University, this list is extended with document which contains data about students’ rating - special parameter that is calculated based on student’s marks.

Currently in the university we already have the system that is used to help the heads of the departments to run all documentation related to studying and marks. However, this system was developed using outdated technology of database management designed by Microsoft - MS Access. At the same time, this system is not distributed among the departments, and is used as standalone application on local machines of the heads of the departments. Moreover, maintenance of some parts of this system left much to be desired. Sometimes one can find data that does not satisfy model of relational database.

On the other hand, there is a system called “Contingent” which is used to process all data related to students: admission to the university, expulsion, etc. It was developed with modern and popular in CIS technology called “1C:Enterprise”. It provides all necessary mechanisms and primitives to create software specific to particular business sphere. Moreover, there are special types of data storages that are developed to perform data analysis against it. At the same time “1C:Enterprise” hides all details related to communication with database which helps to define convenient information model without thinking about how to implement this model in a database.

Based on this, there were three different ways to develop a new system of maintenance of studying process: fix disadvantages of inherited systems; use “1C:Enterprise”; or use absolutely new technologies. We decided to use “1C:Enterprise” because it contains everything that was supposed to be a part of the system in future. Moreover, if we decide to use new technologies we should create some additional system or subsystem of data transfer between newly created product and “Contingent”. We must do this because automation of studying process needs information about students. Therefore, the investigation resulted in conclusion to implement the system as part of the “Contingent”.

The project consists of two stages – development and preparing the system for usage. Let us consider features and problems that are related to development process.

First of all, we needed to formulate acceptance criteria of developed system. Based on experience of mentioned system where we found many problems with data in database it was decided to put principles of reliability and correctness of data in the foundation of this system. Fortunately, “1C:Enterprise” has built-in programming language that may be used to express rules of data validation via code. The next criterion is the intelligent processing of existing data in order to show relevant information as hints. To implement it we can also use mechanisms of “1C:Enterprise” that allows us easily find information and has a lot of built-in functionality to work with standard components of a system. The next criterion is accumulation of huge amount of data from different sources to perform analysis.

Second, we needed to define expected components to be created. First of all, documents should reflect existing ones, but, at the same time, we added additional documents to make relations between separated parts of business process more understandable. For instance, we introduced a new document that contains estimated number of students to be taught in the university next year. It does not have analogue in real life but it will help us to analyze efficiency of departments in future.

In addition, we understood that users need some additional components to have retrospective information about students’ progress. After some investigation we found out that users need not only information about one student but also about groups of students. System should also provide data about students that have missed assignment. As a result, all required information is provided in the form of data processors – it is also a feature of “1C:Enterprise”; such components may contain some custom logic and interface that cannot be a part of existing items or have no equivalent as a document.

Finally, since one of acceptance criteria is analysis of big amount of data we added special data storages, which have built-in mechanisms of indexing and optimization queries to data. They are called registries in terms of “1C:Enterprise”. We added registries, which contain information about students’ marks and rating. Since registries have quick access to data and the university deals with a lot of reports, we decided to add several ones to prove that the system can be used not only to maintain data but also to perform complex data selection and representation on daily basis.

After the development was finished, the system needed to be prepared for usage. On this stage, we faced with the lack of existing data in the system. We could start to use the system to work with data on first year students, but the system did not have any information about students of higher courses. Therefore, we needed to load data from existing systems; moreover, this process included not only data extraction, but also conversion it to internal types and validation of loaded data. Such sequence of operations is called ETL(Extract, Transform, Loading). ETL is the common name for a group of methods that is widely used in OLAP-systems.

There are two different ways to implement ETL-process: use existing products or implement our own solution using built-in programming language. Since we did not have different source of data and we needed to perform loading once, it was decided to create own loading system and implement it as a data processor. Since we knew structure of existing systems’ databases, we used direct, raw SQL queries to load data in the data processor. After extraction the data is grouped into components of the system: curriculum, reports with marks and rating list. After that, these items are loaded into the system according to strict sequence of loading: curriculum, reports with marks and rating list.

The important part of loading is data cleaning. As I mentioned above the system is supposed to be a source of big amount of reports which are checked by controlling authorities, so one of the most important requirements is correctness and reliability of data. To satisfy such criteria we already have mechanisms of self-verification and self-analysis to find wrong data inside the documents and other components. Firstly, data is cleaned at transformation step where the system corrects wrong data such as redundant or duplicated records, incorrect information about discipline in curriculum and so on. Then, on loading step, the system uses built-in mechanisms of self-validation. User performs the final verification. He or she compares reports generated by the system (based on loading data) with reports, which are stored in the university, or reports generated by old systems.

In conclusion, I would like to say that all goals and targets were reached. Now I am sure that the system has all critical characteristics, which were mentioned as acceptance criteria. Moreover, I can strongly say that accumulation of information in the corporate database allows us to conduct huge amount of different analytical researches to find approaches to increase efficiency of studying process in Ivanovo State Power University, to get full information about dynamics of students’ marks in different selections of data for long period. Furthermore, this data will help us to automate other parts of university routine based on accumulated data.

Литература:

1. Орешков В.И. Бизнес-аналитика: от данных к знаниям: Учебное пособие. 2-е изд., испр. / В.И. Орешков, Н.Б. Паклин – Спб.: Питер, 2013. – 704 с.

2. Радченко М.Г. 1С:Предприятие 8.2 Практическое пособие разработчика / М.Г. Радченко, Е.Ю. Хрусталева. – М.: ООО «1С-Паблишинг», 2009. – 872 с.