# PRANAS: A Process Analytics System Based on Process Warehouse and Cube for Supply Chain Management
## 2 Related Work  
The integration between business and process analytics has been considered in different ways in a few studies [4; 5; 6; 9; 10]. A conceptual framework of a decision support system for business intelligence was presented by [PERSON] and [PERSON] [4], which combined classical data-centric approaches such as data mining with a modern system engineering using the advanced concept of adaptive business intelligence. [PERSON] argued that, in service-oriented industries, the operational business intelligence and business process management systems need to be integrated with case-handling [5], which could be done with the framework proposed in this paper. [PERSON] presented an architecture to functionally integrate data analysis functions and process discovery functions [6]. Both systems coexist in what it is called a process-aware enterprise organization, which is usually the case of companies that participate in supply chain systems. [PERSON] et al. handled the business and process analytics integration problem based on a process graph, which refers to large hybrid collections of heterogenous and partially unstructured process execution data [9]. They presented the process OLAP (P-OLAP), which is a very similar concept to the process cube presented by [PERSON] [8]. However, P-OLAP focuses on the scalability of big process graphs operations based on MapReduce rather than the actual integration of process and business analytics as it is presented in this paper. In addition, [PERSON] et al. presented a decision-support oriented framework that integrates process analytics with other types of analytics for project selection [10]. The integration is achieved by decomposing a complex process into sub-manageable processes, which, in turn, are handled with multiple analytic techniques such as text mining, clustering, and social network analysis. However, the approach is limited to recommendation analysis. More specifically, a few studies have integrated business analytics directly with process mining [11; 12]. An approach to support business and IT users in the task of measuring and monitoring the performance of business process execution was presented by [PERSON] et al. [11]. They described well important concepts, such as process behavior and process state change, and even they described their own definition of process mining. However, when the paper was written, process mining techniques were not as mature as they are today. We can benefit from many mature process mining algorithms that give our approach better foundations and wide applicability, such as process discovery algorithms, conformance checking, and decision point analysis. [PERSON] et al. presented an interesting attempt to integrate OLAP concepts and capabilities into process analysis [12]. Their approach focused on modeling a data warehouse and presented a formal solution for this problem by means of an extended snowflake schema that is quite complex. In our study, the schema becomes much simpler, since our approach leverages the advantages of process cubes which already integrate the OLAP concept with business process elements such as event class and case type. Process cube, which is a relatively new concept, has been implemented in some domains such as education [13; 14], resource allocation [15], and internal logistics [16]. Applications in the education domain were described in case studies based on video lectures from a Dutch university [13; 14]. The nature of the data makes process cubes suitable for analyzing the video lecture-watching behavior depending on characteristics of the students (e.g., country, age, gender, school year), because the ability to slice and dice depending on different attributes of the process data yields meaningful results. Additionally, the authors implemented an automated process to generate periodic reports, which can be used by the lecturers to monitor and correlate the students' behaviors with their grades.