Finding top-k relevant groups of spatial web objects

2015 ◽  
Vol 24 (4) ◽  
pp. 537-555 ◽  
Author(s):  
Anders Skovsgaard ◽  
Christian S. Jensen
Keyword(s):  
Web Objects

Developing Schemas for the Location of Common Web Objects

2001 ◽  
Vol 45 (15) ◽  
pp. 1161-1165 ◽  
Author(s):  
Michael L. Bernard
Keyword(s):  
Web Objects

scholarly journals Mental models for web objects: Where do users expect to find the most frequent objects in online shops, news portals, and company web pages?

2010 ◽  
Vol 22 (2) ◽  
pp. 140-152 ◽  
Author(s):  
Sandra P. Roth ◽  
Peter Schmutz ◽  
Stefan L. Pauwels ◽  
Javier A. Bargas-Avila ◽  
Klaus Opwis
Keyword(s):  
Mental Models ◽  
Web Pages ◽  
Web Objects ◽  
Online Shops

scholarly journals Web Objects Ambient: An Integrated Platform Supporting New Kinds of Personal Web Experiences

2016 ◽  
pp. 563-566 ◽  
Author(s):  
Gabriela Bosetti ◽  
Sergio Firmenich ◽  
Gustavo Rossi ◽  
Marco Winckler ◽  
Tomas Barbieri
Keyword(s):  
Web Objects ◽  
Integrated Platform

You Never Get a Second Chance to Make a First Impression

2011 ◽  
pp. 221-235
Author(s):  
Javier A. Bargas-Avila ◽  
Sandra P. Roth ◽  
Alexandre N. Tuch ◽  
Klaus Opwis
Keyword(s):  
Mental Models ◽  
Online Store ◽  
Shopping Cart ◽  
Interaction Quality ◽  
Web Objects ◽  
First Impression ◽  
Search Field ◽  
Online Shops ◽  
Second Chance

Knowledge about users’ expectations and mental models is a key aspect of interface development. By meeting users’ expectations, errors may be prevented and interaction quality enhanced. In the case of online shops, this means that it is crucial to know where users expect to find the most common Web objects such as the search field, shopping cart, or navigation. In this chapter, we show how users’ mental models of an online shop can be analyzed and validated empirically. The resulting model shows where users typically expect to find the most common Web objects within an online store. This knowledge can be used to improve the first impression, orientation, and usability of your website.

Server-Assisted Bandwidth Negotiation Mechanism for Parallel Segment Retrieval of Web Objects

2005 ◽  
pp. 883-890
Author(s):  
Chi-Hung Chi ◽  
Hongguang Wang ◽  
William Ku
Keyword(s):  
Web Objects ◽  
Negotiation Mechanism

scholarly journals A Survey of Data Mining Techniques on Information Networks

2018 ◽  
Vol 7 (2.6) ◽  
pp. 293
Author(s):  
Sadhana Kodali ◽  
Madhavi Dabbiru ◽  
B Thirumala Rao
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Information Networks ◽  
Information Network ◽  
Heterogeneous Information ◽  
Data Mining Techniques ◽  
Heterogeneous Information Networks ◽  
Web Objects ◽  
The Social ◽  
Social Media Network

An Information Network is the network formed by the interconnectivity of the objects formed due to the interaction between them. In our day-to-day life we can find these information networks like the social media network, the network formed by the interaction of web objects etc. This paper presents a survey of various Data Mining techniques that can be applicable to information networks. The Data Mining techniques of both homogeneous and heterogeneous information networks are discussed in detail and a comparative study on each problem category is showcased.

Using of Web Objects method in agile web software projects

2013 ◽  
Author(s):  
Stipe Celar ◽  
Zeljko Seremet ◽  
Zeljko Marusic ◽  
Mili Turic
Keyword(s):  
Software Projects ◽  
Web Objects

scholarly journals Microservices in Web Objects Enabled IoT Environment for Enhancing Reusability

Sensors ◽  
2018 ◽  
Vol 18 (2) ◽  
pp. 352 ◽  
Author(s):  
◽  
◽  
◽  
Keyword(s):  
Web Objects

An Empirical Comparison of Function Points and Web Objects

2009 ◽  
Author(s):  
Giulio Barabino ◽  
Guido Porruvecchio ◽  
Giulio Concas ◽  
Michele Marchesi ◽  
Roberto De Lorenzi ◽  
...  
Keyword(s):  
Empirical Comparison ◽  
Function Points ◽  
Web Objects
Sign in / Sign up

Export Citation Format

Share Document