Proceedings of the 18th International Database Engineering & Applications Symposium

Databases are now part of our everyday lives even if at times not explicitly. Organized collections of data provide information, so important in decision making, from the medical area to business. Web mining is important to improve human computer interaction in general and in particular to exploit information available on the Internet. This area benefits from knowledge, concepts and techniques from artificial intelligence, statistics, linguistics and graph theory, among other fields. Everyday we stumble upon many different kind of data, arising from different sources. The combination of data from several sources, stored using different technologies, provides a unified view of the data and empowers data processing and analysis. Making data meaningful and worthy in a particular context is an imperative task. The logical structure of data is essential for the correct and efficient storage, organization and processing of data. Current technological developments allow the collection of huge amounts of data that can take decision-making processes to new levels. However, this is only possible if data can be transformed into knowledge. Various kinds of data mining algorithms are used to extract data patterns. The development of data preparation techniques is both a challenging and critical task. The amount of private and personal data contained in databases has grown radically with the current digitalization of our lives. Moreover, the access to databases is widespread and made easier by the interconnection of information systems. Database systems must be designed in a way that limits the disclosure of private information. Nowadays, business intelligence applications are widely used in organizations and their strategic importance is clearly recognized. The dissemination of data mining tools is increasing in the business intelligence field, as well as the acknowledgement of the relevance of its usage in companies. Also, cloud computing relies on sharing computing resources rather than having local servers or personal devices to handle applications. It enables collaborative work and gives cheaper and continuous access to computational resources. Automatic data collection and retention of end user actions has become the norm. Typical approaches in mobile crowd sensing applications collect and process sensor data on devices and apply local analytic algorithms to produce consumable data for users. Web crowd-sensing can also contribute with detailed data where proprietary data are extremely costly.