Conceptual frame work for data stream processing Sample Clauses

Conceptual frame work for data stream processing. We expect that the tools we mentioned in the ISP1-3 use cases generate large quantity of data by the interaction of users during their cooperation/collaboration sessions. However, the use of such data is not viable in decision-making processes, due to its high amount. Generally, such processes require already transformed data that aggregates a certain value and therefore can support decision makers performing their tasks. In this section we intend to introduce a conceptual framework to transform raw data into processed information that can be presented to the user while taking decisions or to provide as input to software agents that are able to decide autonomously. This framework is detailed in picture 1. The framework is composed of 4 layers: raw data, derived data, validated data and action. Additionally, the framework also contains the description of the raw data, represented by the Meta Data entity. The characteristics of each entity will be detailed in the next paragraphs. The Raw Data represents the data that is generated by the collaboration tools and by the interaction of its user. Such kind of data can be generally interpreted as the logging data of the system. For example, two participants of a chatting session – Xxxxxx and Xxxxxxx – can exchange some messages to schedule a meeting. In this case, we intend to store the information that is being exchanged by the participants. The information transmitted through the data streams just represents the amount of data generated by the collaboration tools. As our major goal is to support different kinds of adaptation within our tools, we need to gather as much data as possible besides the outcomes of the raw data. At this point we are interest in collecting information about entities that are related to the outcomes of the raw data layer, in this case: the tools that are being used, the users that employ those tools and the processes that are carried out from the users. We classify those three variables that we just mentioned as being part of our Meta Data layer. One of the goals of this framework is to facilitate the use of data to be processed by an analyst to ease the decision-making process. As the collaboration data streams contain high amounts of information (coming from the raw data) it would be very complex for a human analyst to process all of the raw data without any filtering mechanism that can transform the high amounts of data into processed information, reducing its volume. That is why the ...
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