Hi Saira,

process mining and text miming are two different topics.

Process Mining is the extraction of knowledge from logs (eg. logs of a document management server) with the aim of process analysis. So it is possible to study the process and make some improvements thanks to the knowledge extracted from logs.

Text Mining is the extraction of knowledge from any text, principally from web. An example of text mining system can be this : a crawler detects some relevant links on the web, these web contents are analyzed by a parser, the application of datamining algorithms/pattern recognition extracts knowledge related to the goal (eg. text clustering, sentimental analysis).

i wrote this :-) and in that paper i used business process' knowledge(which is provided in text form) for ontology learning purpose but now i am writing another paper in which want to clarify between these two fields and before writing my views i want to know opinions of different researchers is it wrong way?

Hi Saira, The basic difference between the two should be explored at three main levels: 

1. type of data and the mode of data collection

2. Data Analysis tools are different for each

3. Business outcome / purpose of analysis

There is course going on coursera.org on process mining which will help in better understanding. Take a look

Hi Saira, both are different and following are some of the differences:

1. Text mining involves unstructured data like long sentences and comments while process mining typically has structured fields in event log such as caseID, activity, timestamp and actor. However, you may have to perform text mining to extract the event log. Similarly, if you want to perform advanced analysis, you can have more information like description associated with each activity in log. In that case, you will apply text mining on the description field specifically to infer useful insights. 

2. Process mining can be done from multiple perspectives like control flow, and organizational but the main focus remains overall process improvement. Whereas, text mining is more generic and applied on any text data with difference objectives.

Hope it is helpful.