۱٫ Main text

۲٫ Related Works

۳٫ The Project Definition and its Impact in the Processing Architecture

۴٫ The New Structural Coefficient based on the Definitions of the Attributes

۵٫ Implementation of the Text Similarity-Driven Structural Coefficient

۶٫ Conclusions

Acknowledgement

References

The real-time data processing constitutes a critical area when talking about real-time decision making. Strong decisions are based on recommendations for describing the associated course of actions, but the real-time processing gives a very short time for searching them. The Processing Architecture based on Measurement Metadata is a data stream engine oriented to measurement projects, which supports the decision making through an organizational memory. The search space related to the organizational memory is initially in-memory limited using the structure of the measurement projects. Given a project, the related projects are ordered based on a given scoring from its structural definition. Here, a new structural coefficient based on the text similarity, which is computed from the textual definition of each descriptive attribute of a project is introduced. This allows better scoring of the related projects, even when its definitions could be affected by human errors or multiple definitions. The scoring is critical when in a given situation, a project has not specific experience for recommending, in such context, the recommendations from the near projects are served. The pabmm_sh library is outlined and a simulation on its associated processing times for the similarity computing are introduced based on the token definition for a measurement project. The library adds a new alternative perspective in the processing architecture for driving the searches into the organizational memory. It can update 2000 projects less than 1 second, keeping the individual processing time of each project under 1 millisecond.

Main text

The data processing is cheaper every day thanks to the technology evolution jointly with the scale economy, even the big data repositories and the data streams are fed allowing the data-driven decision making as a natural aspect in the different organizations (e.g. the governments) [1]. The measurement and evaluation constitute a logic step for determining the current state of a concept under monitoring and its posterior evaluation. One of the applications of the data-driven decision making is the monitoring of entities, which is especially useful for keeping track of an entity (e.g. a person), jointly with the characterization of its behavior [2].