Computer Science

Traffic jam is a crucial issue affecting cities around the world. They are only getting worse as the population and number of vehicles continues to increase significantly. Traffic signal controllers are considered as the most important mechanism to control the traffic, specifically at intersections, the field of Machine Learning offers more advanced techniques which can be applied to provide more flexibility and make the controllers more adaptive to the traffic state. Efficient and adaptive traffic controllers can be designed using a multi-agent reinforcement learning approach, in which, each controller is considered as an agent and is responsible for controlling traffic lights around a single junction. A major problem of reinforcement learning approach is the need for coordination between agents and exponential growth in the state-action space. This paper proposes using machine learning clustering algorithm, namely, hierarchal clustering, in order to divide the targeted network into smaller subnetworks, using real traffic data of 65 intersection of the city of Ottawa to build our simulations, the paper shows that applying the proposed methodology helped solving the curse of dimensionality problem and improved the overall network performance.

In the present computing world, most new-generation database applications need for intelligent interface to enhance efficient interactions between database and the users. Database query language SQL could be difficult to the non-expert users and learning these formal queries takes a lot of time. In this paper, we discussed mapping of natural language queries to SQL rather than building normal query and a user simply poses the question in everyday verbiage language. Furthermore, we introduce a dynamic approach to determine the tables and attributes involved in the query by using database metadata schema. Our approach will minimize the time that used to build the queries thus minimizing the code size and effort for building query. We also propose a dynamic component that helps in building any NLIDB this component is linked to all the NLIDB system's components and helps to build the dictionaries and database schema graph. KEYWORD Intelligent Multidimensional Database Interface (IMDI), Natural Language Interface to Databases (NLIDB), Database Management System (DBMS), Structured Query Language (SQL). ——————————  ——————————

—Database query language could be difficult to non-expert users and learning formal queries takes a lot of time. Accessing a database requires machine-readable instructions that not everybody is supposed to know, they should be able to ask a question in natural language without knowing either the underlying database schema or any complex structured machine language. Also, we can type a question or a sentence in their natural language. We will use mapping between natural language and the database SQL query. In this paper, we will use the coverage criteria for evaluating the adequacy of a test suite for SQL queries that retrieve information from the database that join information from different tables and whish, selected data is further processed. We will use automatic mapping of questions into SQL queries. We apply test case on AdventureWorks2012 database and use different condition coverage criteria for representing all possible combinations and results of evaluation conditions for a SQL query then coverage criteria used to develop test inputs queries from a real-life application.