# On a Certain Research Gap in Big Data Mining for Customer Insights
## 4 Challenges in Big Data Mining for Customer Insights
This is quite natural because events are the building blocks of surroundings of organizations; hence, they need to be represented and mined during big data analytics. The big data mining process should be therefore focused on events implied from the massive volume of data. It is thus clear that big data mining is closely related to events [164]. The consecutive big data mining challenges may be formulated as the challenge of event capturing and representation for further analytics, the challenge of constructing temporal big data mining algorithms, and the challenge of representing temporal features of the mined knowledge. The events and temporal information in big data should be identified; the temporal relations among events should be found and represented; event-based information retrieval and analytics should be done. [PERSON] et al. [77] proposed a big data temporal analytics solution but only for texts, while leaving apart many other forms of data leading to the big data variety feature. Another approach to temporal big data has been proposed in the work of [PERSON] et al. [160] where the frequent patterns mining and cluster analysis model are used on constantly incoming data. The model encompasses a progressive and incremental update of mined patterns and clusters with new information, and newly discovered patterns and clusters are incrementally added to the existing ones. However, events are not addressed in this approach, and there is lack of temporal representation. In fact, the model is concentrated on time series instead of event sequences. An answer to the challenge of temporal BD mining has been proposed in [77; 144]. Both approaches consider Complex Event Processing (CEP) systems as a solution. CEP systems are particularly useful for real-time analytics and stream reasoning. These solutions differ in time representation. Some of them are based on point structures, while others are based on intervals (cf. [6]). The CEP systems also differ in their complexity and in orientation: computation-oriented vs. detection-oriented ones [77]. A variation of event processing systems has been proposed in [159], namely, Semantic Complex Event Processing augmented with an agent that dynamically builds an ontology which can then be queried temporally. However, even the mining systems based on event processing are yet not capable of mining causality relations [143] which would contain a lot of useful information on complex phenomena in organization's environment. Another group of approaches to analyzing streaming and/or temporal big data is built upon the so-called ontology-based data access (OBDA). OBDA origins from the Semantic Web analytics, and its core feature lies in separating conceptual and database levels of data [94]. Unfortunately, OBDA itself does not adapt to changes in data sources. The W3C standardized an ontology and a query language for the ODBA: OWL2 QL and SPARQL [165], but these solutions do not handle essentially temporal big data. Incorporating complex temporal information into OBDA together with the ability to process heterogeneous data poses a serious challenge [166]. A temporal OBDA is then requisite. There are various ways to the development of such a temporal version of OBDA:  
* Extending OWL2 QL with various temporal operators [167];
* Extending both OWL2 QL and SPARQL with the LTL (linear temporal logic) temporal operators [168; 169];
* Extending OWL2 QL and SPARQL with the MTL (metric temporal logic) temporal operators [165];
* Extending OWL2 QL and SPARQL with the interval logic by [PERSON] and Shoham [170];
* Developing fully temporal OBDA [170]. The advantage of all the above solutions lies in the direct incorporation of time dimension into analytics. On the other hand, the main disadvantage and weakness in the context of big data mining concern the nature of data and analytics. All the above solutions are directed towards relational/structured data and queries and do not deal with any data mining tasks. Hence, they cannot be considered satisfactory for temporal big data mining.