Autonomic collaborative RSS: An implementation of autonomic data using data killing patterns

Corporate and personal computers are flooded by a huge amount of data. Among them, there are irrelevant, similar, false, wrong and obsolete data. Besides, the treatment of this data is relatively complex. Systems need to check, transform, adapt, and summarize data in order to use it. These activities spend time and money of many companies that should be concerned for business rules. In RSS feeds, we have these problems: the users receive a big number of news, sometimes irrelevant or duplicated. The tools to cope with these data do not provide efficient mechanisms to manager information overload. To reach this goal, it is necessary to introduce a new complexity to feed readers, what is sometimes undesirable. Therefore, we propose the autonomic collaborative RSS that transfers the system complexity to data, facilitating the system development. At the same time, it allows to incorporate data treatment rules, as well, data filtering through data killing patterns.