JeDEM 6(1): 69-79, 2014

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Figure 5: Example of semantic enriching in ODR
In the last entity import step, the user can indicate the license of entities to import and other
metadata to publish to CKAN. Updates and insertions are then committed to entity storage and a
new semantified resource is published on CKAN. The resource will contain the provided metadata
and a reference to imported entities in the entity storage.

5. Open Big Data
Nowadays big volumes of data are processed at an increasing rate, creating additional hidden
information. This information represents a central aspect in the definition of Big Data that can be
defined as Value. In fact, according to some industry analysts, dealing with Big Data means facing
the following aspects: Volume (huge amount of data generated or data intensity that must be
ingested, analyzed, and managed to make decisions based on complete data analysis), Velocity
(the speed at which data must be processed), Variety (the different types and sources of data that
must be analyzed and the complexity of each and the whole), Variability (intended as the inherent
“fuzziness” of data, in terms of its meaning or context) and indeed, last but not least, Value. Since
the public sector is increasing the quantity of data available to the public through many open data
initiatives, we expect that in the near future also collected data by these initiatives will thrive by the
adoption of BigData technologies to gather useful information from published data.
As part of OGD initiative of the PAT, we then focus on the problem of data explosion and the
consequent need of having fast and scalable solutions for storage and analysis. We estimate the
trend for growth will be up to hundred times per year, easily reaching the order of TB of data in few
years from now. For instance the Trentino portal already have sensors based datasets, such as
weather, traffic sensors, real time energy consumption and few others that, they alone already
provide few GB of data per day if collected. Due to this very nature, they pose challenge in using
traditional relational database management systems to handle them and at the same time appear
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14
15
as a problem to be dealt with the Big Data technologies such as Apache Hadoop , Hive , Pig
and NoSQL databases. A kind of big data generated by various actors including government
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http://hadoop.apache.org

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https://hive.apache.org

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https://pig.apache.org

CC: Creative Commons License, 2014.