Why do we need the XES Standard?
The goal of the eXtensible Event Stream
(XES) Standard is to standardize a language to transport,
store, and exchange (possibly large volumes of) event data
(e.g., for process mining).
The spectacular growth of the digital
universe, summarized by the overhyped term “Big Data,” makes
it possible to record, derive, and analyze events. Events may take
place inside a machine (e.g., an X-ray machine, an ATM, or baggage
handling system), inside an enterprise information system
(e.g., an order placed by a customer or the submission of a
tax declaration), inside a hospital (e.g., the analysis of a
blood sample), inside a social network (e.g., exchanging e-mails or
twitter messages), inside a transportation system (e.g., checking
in, buying a ticket, or passing through a toll booth), etc. Events
may be “life events,” “machine events,” or “organization events.”
The term Internet of Events (IoE), refers to all event data available.
The IoE is composed of:
- The Internet of Content (IoC):
all information created by humans to increase knowledge on
particular subjects. The IoC includes traditional web pages,
articles, encyclopedia like Wikipedia, YouTube, e-books,
- The Internet of People (IoP): all data related
to social interaction. The IoP includes e-mail, Facebook,
Twitter, forums, LinkedIn, etc.
- The Internet of Things
(IoT): all physical objects connected to the network. The IoT
includes all things that have a unique id and a presence in an
- The Internet of Locations (IoL):
refers to all data that have a geographical or geospatial
dimension. With the uptake of mobile devices (e.g.
smartphones), more and more events have location or movement
Note that the IoC, the IoP, the IoT,
and the IoL are overlapping. For example, a place name on a
webpage or the location from which a tweet was sent. Process
mining aims to exploit event data in a meaningful way, for example,
to provide insights, identify bottlenecks, anticipate problems,
record policy violations, recommend countermeasures, and streamline
processes. This explains our focus on event data.
mining is an emerging discipline providing comprehensive sets of
tools to provide fact-based insights and to support process
improvements. This new discipline builds on process model-driven
approaches and data mining. Process mining provides a generic
collection of techniques to turn event data into valuable insights,
improvement ideas, predictions, and recommendations. The starting
point for any process mining effort is a collection of events
commonly referred to as an event log (although events can also be
stored in a database and may be only available as an event
stream). A wide range of process mining techniques is available to
extract value and actionable information from event data.
Process discovery techniques take an event log or event stream
as input and produce a process model without using any
a-priori information. Conformance checking can be used to check
if reality, reflected by the event data, conforms to a
predefined process model and vice versa. Process mining can also
be used to extend process models with performance-related
information, e.g., bottlenecks, waste, and costs. It is event possible
to predict problems and suggest actions.
Currently, there are
over 25 commercial process mining tools. In fact, the adoption of
process mining has been accelerating in recent years. Tools like Disco (Fluxicon), Celonis Process
Mining, ProcessGold Enterprise Platform, Minit, myInvenio,
Signavio Process Intelligence, QPR ProcessAnalyzer, LANA Process
Mining, Rialto Process, Icris Process Mining Factory, Worksoft
Analyze & Process Mining for SAP, SNP Business Process
Analysis, web-Methods Process Performance Manager, and Perceptive
Process Mining are now available. Moreover, open source tools
like ProM, ProM Lite, and RapidProM are widely used. It is vital
that event data can be exchanged between these tools. Several of
these tools already support XES. For example, it is easy to
exchange XES data between Disco, Celonis, ProM, Rialto Process,
minit, and SNP.