Mario Jose Divan

Towards a Data Processing Architecture for the Weather Radar of the INTA Anguil

The Weather Radar (WR) of the Experimental Agricultural Station (EAS) INTA Anguil produces daily a volume of 17GB of data, which represents about 6.2 Tb annually. The use of such data when they are generated, as well as its subsequent management, use and the possibility of providing services to the public represent a challenge in terms of volume and complexity. The Strategy for Data Stream Processing based on Measurement Metadata (SDSPbMM) is a data stream manager sustained in a measurement and evaluation framework, which incorporates detective and predictive behavior, through the use of measurements and associated metadata. This paper proposes a processing architecture that extends the SDSPbMM to incorporate the processing of big data. This would provide the WR of a detective and predictive behavior on online data, as well as include a layer of public services, which encourages the consumption of data generated by the WR of INTA Anguil.

Case based organizational memory for processing architecture based on measurement metadata

With the aim to manage and retrieve the organizational knowledge, in the last years numerous proposals of models and tools for knowledge management and knowledge representation have arisen. However, most of them store knowledge in a non-structured or semi-structured way, hindering the semantic and automatic processing of this knowledge. In this paper we present a summary of an case-based organizational memory ontology, which aims at contributing to the design of an organizational memory based on cases, so that it can be used to learn, reasoning, solve problems, and as support to better decision making as well. The objective of this Organizational Memory is to serve as base for the organizational knowledge exchange in a processing architecture specialized in the measurement and evaluation. One key aspect associated with the measurement process is that the measures must be consistent and comparable in any moment for making decisions properly. In this way, the processing architecture is based on the C-INCAMI framework (Context-Information Need, Concept model, Attribute, Metric and Indicator) to define the measurement projects. Additionally, the proposal architecture uses a big data repository to make available the data for consumption and to manage the Organizational Memory, which allows a feedback mechanism in relation with online processing. The relation between the data stream processing, the big data repository and the Organizational Memory will be shown. In order to illustrate its utility a practical case associated with the weather radar (WR) of the Experimental Agricultural Station (EAS) INTA Anguil (La Pampa State, Argentina) is shown. Also future trends and concluding remarks are outlined.

Processing architecture based on measurement metadata

In this invited talk, a processing architecture specialized in the measurement and evaluation will be shown. One key aspect associated with the measurement process is that the measures must be consistent and comparable in any moment for making decisions properly. In this way, the processing architecture is based on the C-INCAMI framework (Context-Information Need, Concept model, Attribute, Metric and Indicator) to define the measurement projects. Thus, the data stream processing and monitoring can be guided by metadata. Additionally, the proposal architecture uses a big data repository to make available the data for consumption and to manage an organizational memory, which allows a feedback mechanism in relation with online processing. Next, the formal processes associated to the processing architecture was documented in SPEM version 2 and will be introduced. From this internal point of view, the relation between the Measurement and Evaluation (M&E) framework, the data stream processing, the big data repository and the organizational memory will be shown jointly with the future works. Finally, the case associated with the weather radar of the Experimental Agricultural Station (EAS) INTA Anguil (La Pampa State, Argentina) will be presented jointly with its technological approach.