Big Data Analytics for Security
Citations
1,267 citations
Cites background from "Big Data Analytics for Security"
...Aggregating these data evidently goes beyond the abilities of current data integration systems (Carlson et al., 2010). According to Karacapilidis, Tzagarakis, and Christodoulou (2013), the availability of data in large volumes and diverse types of representation, smart integration of these data sources to create new knowledge – towards serving collaboration and improved decision-making – remains a key challenge. Halevy, Rajaraman, and Ordille (2006) assert that the indecision and provenance of data are also a major challenge for data aggregation and integration. Another challenge relates to aggregated data in warehouses – in line with this argument, Lebdaoui, Orhanou, and Elhajji (2014) report that to enable decision systems to efficiently respond to the real world's demands, such systems must be updated with clean operational data. • Step 4 – Data Analysis and Modelling: Once the data has been captured, stored, mined, cleaned and integrated, comes the data analysis and modelling for BD. Outdated data analysis and modelling centers around solving the intricacy of relationships between schema-enabled data. As BD is often noisy, unreliable, heterogeneous, dynamic in nature; in this context, these considerations do not apply to non-relational, schema-less databases (Shah et al., 2015). From the perspective of differing between BD and traditional data warehousing systems; Kune, Konugurthi, Agarwal, Chillarige, and Buyya (2016) report that although these two have similar goals; to deliver business value through the analysis of data, they differ in the analytics methods and the organization of the data....
[...]
...Aggregating these data evidently goes beyond the abilities of current data integration systems (Carlson et al., 2010). According to Karacapilidis, Tzagarakis, and Christodoulou (2013), the availability of data in large volumes and diverse types of representation, smart integration of these data sources to create new knowledge – towards serving collaboration and improved decision-making – remains a key challenge. Halevy, Rajaraman, and Ordille (2006) assert that the indecision and provenance of data are also a major challenge for data aggregation and integration....
[...]
...Aggregating these data evidently goes beyond the abilities of current data integration systems (Carlson et al., 2010). According to Karacapilidis, Tzagarakis, and Christodoulou (2013), the availability of data in large volumes and diverse types of representation, smart integration of these data sources to create new knowledge – towards serving collaboration and improved decision-making – remains a key challenge....
[...]
...Aggregating these data evidently goes beyond the abilities of current data integration systems (Carlson et al., 2010). According to Karacapilidis, Tzagarakis, and Christodoulou (2013), the availability of data in large volumes and diverse types of representation, smart integration of these data sources to create new knowledge – towards serving collaboration and improved decision-making – remains a key challenge. Halevy, Rajaraman, and Ordille (2006) assert that the indecision and provenance of data are also a major challenge for data aggregation and integration. Another challenge relates to aggregated data in warehouses – in line with this argument, Lebdaoui, Orhanou, and Elhajji (2014) report that to enable decision systems to efficiently respond to the real world's demands, such systems must be updated with clean operational data....
[...]
259 citations
Cites background from "Big Data Analytics for Security"
...big data, namely big volume, high velocity and variety [141]....
[...]
244 citations
Cites background from "Big Data Analytics for Security"
...ltimedia data generated from ubiquitous 5G IoT devices can be exploited to enable data-related applications, for example, data analytics, data extraction empowered by artificial intelligence solutions [315]. Cloud computing services can offer high storage capabilities to cope with the expansion of quantity and diversity of digital IoT data. However, big data technologies can face various challenges, ran...
[...]
242 citations
193 citations
Cites background from "Big Data Analytics for Security"
...Authors of [7], discuss that enterprises collect security related data for regulatory compliance and post hoc forensic analysis....
[...]
References
262 citations
102 citations
83 citations
54 citations