‘Nine-Step Methodology’ includes following steps:
Choosing the process
Choosing the grain
Identifying and conforming the dimensions
Choosing the facts
Storing pre-calculations in the fact table
Rounding out the dimension tables
Choosing the duration of the database
Tracking slowly changing dimensions
Deciding the query priorities and the query modes
Dimensionality Modeling
Dimensions are the foundation of the fact table, and is where the data for the fact table is collected. Typically dimensions are nouns like date, store, inventory etc. These dimensions are where all the data is stored. For example, the date dimension could contain data such as year, month and weekday.
WHAT IS A FACT TABLE? (EVENT) = verbs?
err.. my assumption, fact table related to the activities
Fact tables contain the data corresponding to a particular business process. Each row represents a single event associated with that process and contains the measurement data associated with that event.
For example, a retail organization might have fact tables related to customer purchases, customer service telephone calls, and product returns. The customer purchases table would likely contain information about the amount of the purchase, any discounts applied and the sales tax paid.
The information contained within a fact table is typically numeric data and it is often data that can be easily manipulated, particularly by summing together many thousands of rows. For example, the retailer described above may wish to pull a profit report for a particular store, product line or customer segment. The retailer can do this by retrieving information from the fact table that relates to those transactions meeting the specific criteria and then adding those rows together.
FACT TABLE GRAIN
When designing a fact table, developers must pay careful attention to the grain of the table — the level of detail contained within the table.
The developer designing the purchase fact table described above would need to decide, for example, whether the grain of the table is a customer transaction or an individual item purchase. In the case of an individual item purchase grain, each customer transaction would generate multiple fact table entries, corresponding to each item purchased.
The choice of grain is a fundamental decision made during the design process that can have a significant impact on the business intelligence effort down the road.
WHAT ARE DIMENSIONS? (Mostly noun)
Dimensions describe the objects involved in a business intelligence effort. While facts correspond to events, dimensions correspond to people, items, or other objects. For example, in the retail scenario, we discussed that purchases, returns, and calls are facts. On the other hand, customers, employees, items and stores are dimensions and should be contained in dimension tables.
Dimension tables contain details about each instance of an object. For example, the items dimension table would contain a record for each item sold in the store. It might include information such as the cost of the item, the supplier, color, sizes, and similar data.
Fact tables and dimension tables are related to each other. Again returning to our retail model, the fact table for a customer transaction would likely contain a foreign key reference to the item dimension table, where the entry corresponds to a primary key in that table for a record describing the item purchased.
Data Mart vs Data Warehouse
OLTP System vs Data Warehousing
| OLTP System | Data Warehouse |
| Operational User | Managerial Users (Decision Makers) |
| Operational | Strategic Decision |
| Pattern – predictable | Pattern – unpredictable |
| Record transaction (e.g. student enrolment) | Analysis Basis |
| Transactional Data | Details to summary data |
| Factual Data | Relationship (insight) |
Computational Thinking TedTalk
I am super excited to see my teacher, Dr Hai Ning Liang from Xian Jiaotong Liverpool University, Suzhou is giving TedTalk. The title is about computational thinking. Computational thinking is about problem solving. The systematic way decompose the process to solve the problem step by step. There are a few ways to make computional thinking interesting while learning in the class:
1. Make is visible by visualize it.
2. Hands on and put creative ideas into creation (e.g. Lego Robot is like the student’s own baby)
3. Collaborative – learning with other to share, encourage and motivate each other.
4. Add some fun elements – healthy competition among the students.
You can learn more from here:
Calling for Papers – Computational Thinking CHBJ (submission: 31 Dec 2017)
Call for Contributing to a Special Issue in Computers in Human Behavior
Call for Contributing to a Special Issue in Computers in Human Behavior
Title of the Special Issue: Innovations and Technologies in Computational Thinking
Nowadays, students need to acquire skills and digital competences in accordance with 21st century needs. The ability to express ideas in a computationally meaningful way is gradually becoming one of the most essential skills for succeeding in the 21st century. Accordingly, computational thinking (CT) is considered as important as the skills of reading, writing, and arithmetic. In light of this growing recognition, ACM, Code.org, Computer Science Teachers Association, Cyber Innovation Center, and National Math and Science Initiative have developed conceptual guidelines for CT education. Research developments over the last few years have proposed new pedagogies, guidelines and resources for the development of computational thinking skills. Intuitive and student-friendly computer programming environments like Alice, Scratch, BlueJay, Greenfoot, Kodu, and educational robotics, as well as new standards and guidelines from CSTA/ACM and ISTE have been widely applied. However, despite the apparent growing body of research in the area, there is limited evidence to support the design of appropriate learning experiences to allow students to adequately develop computational thinking competences.
This special issue intends to attract contributions related to innovations and technologies to support the development of CT. Research areas of particular interest include, but are not limited to:
- Psychological factors affecting the development of CT
- Adaptation and personalization in CT learning environments
- Intelligent support systems for CT
- Design of learning environments fostering CT
- Learning analytics in CT
- Informal learning experiences promoting CT
- Empirical evidence from case studies and evaluation studies ranging from K-12, to tertiary, to lifelong CT education
- Project-based learning/capstone projects in CT education
- Gender equity in CT education
- Experience with innovative pedagogies in learning or/and teaching CT
- Cognitive and metacognitive support in developing CT skills
Timeline
Paper Submission begins: October 1, 2017
End of Paper Submission: December 31, 2017
First Round of Revisions: February 2018
Revised Papers Due: April 2018
Final decisions: July 31, 2018
Anticipated Publication Date: Fall 2018
Paper submission formatting guidelines per the Journal’s Guide for Authors.
Guest editors
Charoula Angeli, Department of Education, University of Cyprus, cangeli@ucy.ac.cy
Michail Giannakos, Department of Computer Science, Norwegian University of Science and Technology (NTNU), michailg@ntnu.no
Computers in Human Behavior – Artifact construction in Learning (submission: 30 April 2018)
Emerging Technologies for Artifact Construction in Learning
Call for Papers for a special issue for the Computers in Human Behavior titled “Emerging Technologies for Artifact Construction in Learning”
Guest Editors:
1) Dr. Antigoni Parmaxi [http://antigoniparmaxi.weebly.com/] 2) Prof. Panayiotis Zaphiris [http://www.zaphiris.com]
Cyprus Interaction Lab (http://cyprusinteractionlab.com/)
Cyprus University of Technology
Emails: antigoni.parmaxi@gmail.com; pzaphiri@cyprusinteractionlab.com
Focus of the Special Issue
The Innovating Pedagogy 2016 report claims that we are at the beginning of a learning revolution (i.e., a new era that builds and extends the impact of technology in learning in new and unanticipated ways). The goal of this special issue is to bring together different facets of emerging technologies and ground their use under the theoretical framework of constructionism. This special issue aims to include not only innovation on the use of different features of emerging technologies, but also on practices and strategies employed by practitioners and instructional designers as well as their impact on human behavior. This special issue is not constrained in the discipline of Technology-Enhanced Learning. Research on interface design, security concerns arising from the use of emerging technologies are also welcome.
Motivation for the SI
Many recent well-known successful educational activities around the world rely on emerging technologies such as social media, virtual and augmented reality, embodied technologies and mobile applications and their impact on human behavior. This special issue echoes the growing research trend towards innovations in the use of emerging technologies and makes an effort to delineate how constructionism and social constructionism ground their use. Constructionism was chosen as a theory of learning, teaching, and design that aligns well with the demands and expectations of computational culture, and emphasizes building, creating and making of shared and meaningful artifacts as a means for gaining knowledge (Papert, 1980; 1993).
Possible topics of interest
This special issue will welcome contributions on the following, though not exhaustive, list of topics:
- How can emerging technologies embody constructionist/social constructionist elements and transform educational activities?
- How can different features of emerging technologies support the construction of an artifact that can be meaningful to its constructors?
- How do learners engage in meaningful artifact construction within different emerging technologies?
- What behavioral patterns do learners display in a constructionist/social constructionist environment?
Timeline for submission:
Submission portal will be open from 15th November 2017 to 30th April 2018
Submission deadline: 30th April 2018
Author notification: September 30th, 2018
Final approval by Editor-in-chief: November 31st, 2018
Expected publication date: Early 2019
Computers in Human Behavior Journal
CHB is a Q1 Journal. It bimonthly peer-reviewed academic publish by elsevier. The most important part is CHB related to my field (HCI) and cyberpsychology (I dont know the existence of this field until today). The editor is Matthieu Guitton (Laval University, Quebec City).
In 2016, the impact factor is 3.435.
