The feature-oriented nature of web services demands a flexible and adaptive architecture that can accommodate the changes and enhancements to the features supported by such Services. However, due to the increase of new services paired with the dynamic nature of business environment leads to some undesirable interactions that cause a negative impact on Service Quality and User Satisfaction. Such undesirable interactions are called ''Feature Interactions'', a significant area of research highly explored by the worldwide research community of Telecommunication systems, where features (additional units of functionality) would interfere with each other and cause some unpredictable behavior.
Feature Modeling is an efficient and flexible modeling approach, basically used for identifying commonalities and differences among all possible potential products of an SPL. The output of feature modeling
is a compact representation of all potential products of an SPL, called ''Feature Model''. A feature model represents different ways in which a software system can be composed in terms its associated features and
relations among them. A valid composition of the features is called a configuration.
However the basic feature models only deal with modeling the functional characteristics provided by a Software system called ''functional features'' , and doesn't support the modeling
non-functional/quality of service characteristics. If these characteristics could modelled properly, this would increase the number of potential products of a software systems such as an SPL. To overcome these short-comings
of feature models, the basic feature models are modified by adding the notion of ''extra-functional/QoS'' features. The enhanced feature models were termed ''Extended Feature Models'' and they supported the modeling of
extra-functional features as well. The modeling of such QoS/ non functional features allows QoS-driven service selection and composition.Read More!
Process mining techniques allow for extracting information from event logs. For example, the audit trails of a workflow management system or the transaction logs of an enterprise resource planning system
can be used to discover models describing processes, organizations, and products. Moreover, it is possible to use process mining to monitor deviations (e.g., comparing the observed events with predefined models
or business rules in the context of SOX). For more information and current research in Process Mining, please Follow This Link!
Formal Methods--is essentially that area of Computer Science which uses mathematical notations and ideas (like 'proof') for developing designs for software and then exploring their properties mathematically - just as civil and mechanical engineering have calculus, so software engineering has formal methods.
Here is an excellent source of information on Formal Methods at The Oxford Formal Methods archives.
The term Web Data Mining is a technique used to crawl through various web resources to collect required information, which enables an individual or a company to promote business, understanding marketing dynamics, new promotions floating on the Internet, etc.
There is a growing trend among companies, organizations and individuals alike to gather information through web data mining to utilize that information in their best interest.
Data Mining is done through various types of data mining software. These can be simple data mining software or highly specific for detailed and extensive tasks that will be sifting through more information to pick out finer bits of information.
For example, if a company is looking for information on doctors including their emails, fax, telephone, location, etc., this information can be mined through one of these data mining software programs.
This information collection through data mining has allowed companies to make thousands and thousands of dollars in revenues by being able to better use the internet to gain business intelligence that helps companies make vital business decisions. Here is nice resource about web mining research at Web Data Mining Community!.