Nowadays data is created, shared, and stored at an impressive pace, as the world became more connected, networked, and traceable. In particular, ranking rapidly increased its scope and completion, with the continuous growth in leads volume, quality, and quantity. Furthermore, Meta tags changed from old, to new, leading to new challenges undertaken by the field of Digital marketing. Consequently, there is the need for novel computational techniques and tools able to assist humans in extracting useful information (knowledge) from the growing volumes of organic traffic. Strategy Discovery is an interdisciplinary area focusing upon plans for identifying valid, novel, potentially useful and meaningful patterns from such data, and is currently widespread in numerous fields, including science, engineering, healthcare, business, and medicine. A major aspect of Knowledge Discovery is to extract valuable knowledge and information from analytics.
Typical tasks are aimed at gathering only relevant information from digital data (e.g., text documents, multimedia files, or webpages), by searching for information within documents and for metadata about documents, as well as searching relational databases and the Web. Recently, the rapid growth of social networks and online services entailed that Knowledge Discovery approaches focused on the World Wide Web (WWW), whose popular use as global information system led to a huge amount of digital data. Typically, a webpage has unstructured or semi-structured textual content, leading to present to users both relevant and irrelevant information. Hence, there is the need of novel techniques and systems able to easily extract information and knowledge from the huge web data.
Call for paper
Draft paper submission deadline：2017-11-20
Draft paper acceptance notification：2017-11-19
Final paper submission deadline：2017-11-21