The analysis of the captured parameters, both in real time and in historical, through the application of Big Data algorithms, is currently a key element to know the state of the systems, evaluate them and be able to make the appropriate decisions for their optimal exploitation.
At ENVIRA IoT we are specialists in the exploitation of data from large sensor networks or industrial systems, with the aim of putting this information to support decision-making at all levels and in all departments. Our experience in a long list of references in various sectors allows us to bring the business and process vision to the data sets, generating information that can be easily analyzed, understood and exploited at all levels.
Integration of heterogeneous data sources
The data captured by the sensors, to be truly useful, must be enriched by external sources that provide context information, transforming the magnitudes into relevant data for the process or, ultimately, for the business. Obtaining the richest and most useful information involves the combination of data from very heterogeneous sources that can range from sensors deployed in the field to external services in the cloud, through industrial control systems, corporate systems (ERP and similar) or public information on the Internet, to mention just a few examples. At ENVIRA IoT we have extensive experience in the integration of data, generally heterogeneous and unstructured, to generate accurate, reliable information with a high potential for direct application to the business.
The recognition of patterns and events is one of the typologies of the most common projects in which we work for the exploitation of data. Detection of anomalous situations in the processes, identification of exceedances of values, identification of relevant events for industrial processes or the segmentation (clustering) of events, are some of the common applications of this type of projects.
Through real-time data analysis (streaming), either with intelligent algorithms (such as neural networks, decision trees or genetic algorithms) or with expert systems (based on rule linking systems), we can detect the situational state of the machinery and the processes, identifying the optimal maintenance actions, in terms of cost and benefit, recommended at each moment.
Of course, in addition to implementing systems and algorithms for the automatic exploitation of the information, we also develop tools to facilitate exploitation by human operators, using rich and intuitive graphical interfaces.
The exploitation of huge amounts of data requires appropriate infrastructure to manage the volumes of data needed to obtain reliable answers in a very specific period of time. To ensure these two characteristics: reliability and response time, in ENVIRA IoT, we develop both algorithms and data exploitation components, using Big Data techniques in the state of the art, both in streaming analysis solutions (real time), and batch, implanting platforms based on lambda and/or kappa architectures in each case.