Short note on Data Mining in Science and Engineering.

-

  Data Mining in  Engineering

Data mining in engineering shares many similarities with data mining in science. Both practices often collect massive amounts of data and require data preprocessing, data warehousing, and scalable mining of complex types of data. Both typically use visualization and make good use of graphs and networks. Moreover, many engineering processes need real-time responses, and so mining data streams in real-time often become a critical component.

Massive amounts of human communication data pour into our daily life. Such communication exists in many forms, including news, blogs, articles, web pages, online discussions, product reviews, twitters, messages, advertisements, and communications, both on the Web and in various kinds of social networks. Hence, data mining in social science and social studies has become increasingly popular. Moreover, user or reader feedback regarding products, speeches, and articles can be analyzed to deduce general opinions and sentiments on the views of those in society. The analysis results can be used to predict trends, improve work, and help in decision making.

Computer science generates unique kinds of data. For example, computer programs can belong, and their execution often generates huge-size traces. Computer networks can have complex structures and the network flows can be dynamic and massive. Sensor networks may generate large amounts of data with varied reliability. Computer systems and databases can suffer from various kinds of attacks, and their system/data accessing may raise security and privacy concerns. These unique kinds of data provide fertile land for data mining. 

  Data Mining in Science

Data mining in computer science can be used to help monitor system status, improve system performance, isolate software bugs, detect software plagiarism, analyze computer system faults, uncover network intrusions, and recognize system malfunctions. Data mining for software and system engineering can operate on static or dynamic (i.e., stream-based) data, depending on whether the system dumps traces beforehand for post-analysis or if it must react in real-time to handle online data. Various methods have been developed in this domain, which integrates and extend methods from machine learning, data mining, software/system engineering, pattern recognition, and statistics. 

Data mining in computer science is an active and rich domain for data miners because of its unique challenges. It requires the further development of sophisticated, scalable, and real-time data mining and software/system engineering methods,

Comments

Post a Comment

Popular posts from this blog

Suppose that a data warehouse for Big-University consists of the following four dimensions: student, course, semester, and instructor, and two measures count and avg_grade. When at the lowest conceptual level (e.g., for a given student, course, semester, and instructor combination), the avg_grade measure stores the actual course grade of the student. At higher conceptual levels, avg_grade stores the average grade for the given combination. a) Draw a snowflake schema diagram for the data warehouse. b) Starting with the base cuboid [student, course, semester, instructor], what specific OLAP operations (e.g., roll-up from semester to year) should one perform in order to list the average grade of CS courses for each BigUniversity student. c) If each dimension has five levels (including all), such as “student < major < status < university < all”, how many cuboids will this cube contain (including the base and apex cuboids)?

-
Image
a) A Snowflake Shema is shown in figure below: b) The specific OLAP operations to be performed: 1. Roll-up on course from course_id to department. 2. Roll-up on a student from student_id to university. 3. Dice on course, student with department ="CS" and university = "biguniversity" 4. Drill-down on a student from the university to student_name. c) N = 4 dimensions The cube will contain 54 = 625 cuboids.
Read more

Suppose that a data warehouse consists of the four dimensions; date, spectator, location, and game, and the two measures, count and charge, where charge is the fee that a spectator pays when watching a game on a given date. Spectators may be students, adults, or seniors, with each category having its own charge rate. a) Draw a star schema diagram for the data b) Starting with the base cuboid [date; spectator; location; game], what specific OLAP operations should perform in order to list the total charge paid by student spectators at GM Place in 2004?

-
Image
  b) The specific OLAP operations to be performed are: 1. Roll-up on date from date id to year. 2. Roll-up on spectator from spectator id to status. 3. Roll-up on location from location id to location name. 4. Roll-up on the game from game id to all. 5. Dice with status= “students”, location name= “GM Place”, and year=2004
Read more

Explain network topology .Explain tis types with its advantages and disadvantges.

-
Network topology Network topology refers to how the nodes and links in a network are arranged. A network node is a device that can send, receive, store, or forward data. A network link connects nodes and may be either cabled or wireless links. Understanding topology types provides the basis for building a successful network. There are a number of topologies but the most common are bus, ring, star, and mesh: A bus network topology is when every network node is directly connected to a main cable. In a ring topology, nodes are connected in a loop, so each device has exactly two neighbors. Adjacent pairs are connected directly; non-adjacent pairs are connected indirectly through multiple nodes. In a star network topology, all nodes are connected to a single, central hub and each node is indirectly connected through that hub.  A mesh topology is defined by overlapping connections between nodes. You can create a full mesh topology, where every node in the network is connected to every other
Read more