Towards the Next Generation Intelligent BPM

Towards the Next Generation Intelligent BPM —— In the Era of Big Data

Xiang Gao, China Mobile Communications Corporation

Background

From BPM to iBPM

A Big Data Perspective on BPM

Embrace the Idea of Big Data

Outline

Conclusion and Future Work

Subscribers (million)

Over 1 million base

stations covering

99% of national

population, and

roaming service to

237 countries and

regions

Base stations

(thousand)

Brief Introduction of CMCC

As the leading Chinese telecommunication company, China Mobile Communications Corporation (CMCC) is also recognized as the world's largest mobile phone operator by subscribers with about 740 million. In 2012, the Company was once again selected as one of the "FT Global 500" by Financial Times and "The World's 2,000 Biggest Public Companies" by Forbes magazine.

Over 700 million

subscribers,

including over 100

million 3G

subscribers

1200TB

The world’s largest teleco BASS

data warehouse

over

The world’s largest daily signaling

data/billing data

100/10 TB over

The world’s largest teleco

business process repository

40000 processes over

State of the Art of BPM

BPM is always recognized by CMCC as a kind of holistic management approach. By borrowing the basic idea of Gartner's hype cycle, we provide a qualitative graphic representation of the maturity, adoption and social application of specific BPM technologies in CMCC’s view.

time

Technology Trigger

Peak of inflated expectations

Trough of disillusionment

Slope of enlightenment Plateau of productivity

Expectations

business artifacts

process fragmentization

workflow pattern and applications

process repository

BPM Suite

modeling language and mutual conversion

process mining

similarity analysis

cloud-enabled BPMS platform

SOA

business process analysis

BPMS evaluation

fragment merging

process models clustering

Ontology based business behavior/rules modeling

distributed engine

More attention has been focused recently on advanced analytical technologies, especially on interdisciplinary collaboration in both research and application.

Great Challenges Arise from IT Consolidation

In the context of IT systems consolidation, such a large number of business processes must cause an extremely arduous task for maintaining and evolving. Accordingly, one must elaborately consider “business process consolidation” first.

business understanding & raw process reconstruction

During more than 10 years IT construction, a large number of business processes have been varied and updated for many times, and thus, have been biased from the original design. To achieve the objective of consolidation, great attention should be paid to deep understanding of business behavior and efficient process reconstruction.

Actually, business analysts have deep understanding of business but cannot design the process models independently without the support of IT staffs. It is really important to provide an efficient approach to assist flexible and agile design of process models for business analysts with the least IT efforts.

redundancy removal & process repository

flexible modeling based on business semantics

Different subsidiary organizations follow unified business specifications and design their own processes. Due to individual management requirements, their processes, even expressing the same business behavior, are usually not exactly the same while having a high degree of similarity. The technology to reduce duplications and make the differences between process models explicit is really important.

complex business logic & recessive rules

The current processes contain so many special and complex business logics, making the design and execution of processes extremely complicated. Formal design of these business logic should be of much consideration.

Example: OA Systems Consolidation

Office Automation (OA) is one of the most important management information systems for governments and enterprises in China, to process and communicate information for daily working of all the users. OA fundamentally refers to supporting document flow, approval, transfer, archive and other enterprise general management business processes.

The OA systems of CMCC have been independently built for more than 10 years by each subsidiary organizations.

more than 3 kinds of OS

more than 4 kinds of RAID

more than 5 kinds of middleware

more than 49 system integrators

more than 240 external interfaces

more than 1000 application modules

… …

more than 8190 business processes

These processes are described differently and each subsidiary may have its own description, mainly based on informal user-definition or natural-language text documents.

It is causing increased architecture heterogeneity, high integration complexity and especially high construction and maintenance cost.

Challenge I: Diversity of OA Process

Different subsidiary organizations follow unified business specifications and design their own processes. Due to individual management requirements, their processes, even expressing the same business behavior, are usually not exactly the same while having a high degree of similarity.

Statistics of OA Process Samples

Source: Jan Mendling. “Metrics for Process Models: Empirical Foundations of Verification, Error Prediction, and Guidelines for Correctness,” LNBIP 6, Springer, 2008

Let u1, u2, u3 express the normalized mean vectors of different subsidiaries, then the auto-correlation matrix R has the form

1 1 1 2 1 3

2 1 2 2 2 3

3 1 3 2 3 3

, , ,

, , ,

, , ,

1 0.9524 0.9909

0.9524 1 0.9337

0.9909 0.9337 1

R R R

R R R

R R R

u u u u u u

R u u u u u u

u u u u u u

where

T

,i j

i j

i j

R u u

u uu u

Within one subsidiary organization, different processes are highly different.

Processes belonging to different subsidiaries have high similarity.

high graph sparsity (edge density=0.07) high control-flow complexity (control-flow complexity = 16.41, when compared with number of tasks) weakly structured (structuredness = 0.61, include 5 arbitrary cycles) … …

Challenge II: Complex Business Logic of OA Process

The OA processes usually contain plentiful complex business logics, where special attention should be paid to both theoretical and practical issues concerning analysis and realization.

registrat

ion

Approval by

Dept.

Manager

Department

Carry Out

Department

Take Dept. COUNTERSIGN fragment for example. It is a common part within most of document approval processes, and can be modeled as parallel multi-instance plus recursive sub-process structure.

There are several difficulties for

current BPM technologies in

attempts to handle such complex

business logics. For example:

1.How to measure the similarity

between two of such models?

2.How to mining such models from

the corresponding execution log?

3.How to analyze the soundness of

such models?

4.How to do indexing and searching

on such models?

5.How to implement execution,

monitoring and conformance…? 6. ……

Challenge III: Big Gap between Business & IT Realization

There is always a big gap: business analysts have deep understanding of business but cannot design the process models independently without the support of IT staffs, even though notation based modeling language is exploited.

Provide business analysts numerous process templates and fragments templates with specific business semantics, automatically extracted from existing process models

Provide intelligent process models and log analysis capability, e.g., similarity, search, merging, mining and so forth

Provide statistics capability for all kinds of process data

Process template

Fragments templates

+

E-Form templates

Organizations and Role

Rules, external services …

Process models

An intelligent configuration tool for process models design is needed to assist business analysts with easy and flexible process models design.

+

+ ......

Scenario of Process Modeling by Business Analysts

Background

From BPM to iBPM

A Big Data Perspective on iBPM


Outline


On the Way from BPM to Intelligent BPM

The IT systems consolidation highly depends on the consolidation of business processes first, where intelligent BPM (iBPM) has been given new impetus by integrating analytical technologies into orchestrated processes.

meet the ongoing need for process

agility, especially for regulatory changes

and more-dynamic exception handling

aim at leveraging the greater availability

of data from inside and outside the

enterprise as input into decision making

facilitate interactions and collaboration

in cross-boundary processes

iBPM

2012~ BPMS

Mid 2000s BPM

2000

Workflow

1990s

Evolution

Mobile Social Advanced

Analytics External Data

+ =

iBPMS

+ BPMS + Cloud

Platform

There are a thousand Hamlets in a thousand people’s eyes.——William Shakespeare

Definition of iBPM from an Industrial Point of View

Analytical

The most prominent feature of iBPM is the capability of advanced analytics. It

integrates with state-of-the-art analytic technologies, including both pre-

analytics and post-analytics.

process model based analysis, such as model decomposition, clone

detection, similarity search etc

historical log and other information based analysis, such as automatic

business process discovery (i.e., process mining), social analysis, intelligent

recommendation, prediction etc

Automatic

The enormous volumes of data require automated or semi-automated analysis

techniques to detect patterns, identify anomalies, and extract knowledge. Take

process consolidation for example. The iBPM should be designed to facilitate

the procedure that automatically reduces duplications and makes the

differences between process models explicit, instead of manual operation.

Like the famous definition of “Big Data” by the three Vs, from CMCC’s perspective, analytical, automatic, agile and adaptive may also constitute a comprehensive definition of iBPM, and they bust the myth that iBPM is only about analytics. In addition, each of the four As has its own ramifications for analytics.

SOCIAL

BLOG

SMART

METER

101100101001

001001101010

101011100101

010100100101

It is worth noticing that achieving of the “4As” features will be given new opportunities in the era of big data.

Agile

The iBPM is expected to simplify the procedure. For example, by

incorporating process fragments with business semantics into design

tool, the efficiency of modeling can be significantly improved and most

of the procedures can be implemented by business analysts with the

least IT efforts.

Adaptive

The dynamic changing of business processes and external data inside

and outside should be flexibly captured and responded by resorting to

not only the adaptive adjusting of the analysis algorithm parameters,

but also the on-demand selection of appropriate algorithms in a

configuration way.

Definition of iBPM from an Industrial Point of View

Background

From BPM to iBPM



Outline


Big Data: Becoming Big Business

Source: Hilbert and Lopez, “The world’s technological capacity to store,

communicate, and compute information,” Science, 2011.

Global installed, optimally compressed, storage

Growth of global data

Trends of global data

Global installed computation to handle information

Source: Oracle, 2012

The birth and growth of big data was the defining characteristic of the 2000s. As obvious and ordinary as this might sound to us today, we are still unraveling the practical and inspirational potential of this new era.

Source: Cisco, 2011; Gartner 2009&2011; IDC, 2012

Network

Analysis

Pattern

Recognition

There is a need for ongoing innovation in techniques that will help individuals and organizations to analyze the growing torrent of big data. A wide variety of technologies has been developed and adapted to aggregate, manipulate, and analyze big data.

Big Data: Advanced Analytical Techniques

Association

Rule Learning Classification Clustering Crowd Sourcing Optimization

Data Fusion Machine Learning Ensemble

Learning

Genetic

Algorithms Neural

Networks

Spatial Analysis Natural Language

Processing Predictive

Modeling

Source: Big data: then next frontier for innovation, competition, and productivity, McKinsey Global Institute, June 2011

Classification

Clustering

Association

Recommendation

For ultra large scale data set and non-real time batch processing, parallel computing is highly recommended after necessary MapReduce type reconstruct of the original algorithm.

For real-time task, in-memory computing or stream computing and incremental computing are recommended to increase processing efficiency.

Apply probability processing method like Bloom filter\LSH can benefit from time and space complexity decrease.

The applicability analysis of various algorithms and models have been given new impetus for practice in specific large scale data set scenarios, mainly according to the time complexity and space complexity evaluation. Algorithms with both data intensive and CPU intensive capabilities composite the primary computational mode for big data complicated analysis tasks.

Big Data: Advanced Analytical Techniques (Deep Insight)

CPU intensive

Data intensive

leader K-means

FP-growth

Hierarchical Model

Apriori GA

CF

Decision

Tree

Naïve Bayes SVM

LVM

Mobile Social

External Data

Source: Big data, Analytics and the Path From Insights to

Value , MIT Sloan Management Review 2011

What does big data really mean in the evolution of BPM? Elegantly stated by the founding father and pioneer long before the introduction of the big data concept. In God we trust; all others must bring data. ——W. Edwards Deming.

Big Data——Catalyst for BPM Evolution

Driven by process data and other related data, it can be a new platform for the R &

D of intelligence based on big data, making Deming's maxims a reality for the operation of future iBPM systems.

Advanced

Analytics

New methods and tools to embed information into business

processes, are making insights

more understandable and actionable.

On the Path from Insight to Action (Data) ——Finding the Needle in the Big Data BPM Haystack

Where is business process data?

From a special point of view, • the complete event log data • the process models in centralized repository • the process cases data • …… of a large corporation (e.g.,CMCC) can all be treated and analyzed as “big data” in the BPM field.

Traditionally, It is universally acknowledged that

While, from a generalized point of view, data describing a set of behavior or task with specific order can all be treated as “process”, such as user clicking on the web, searching …… Accordingly, a large number of such kind of data are all “big data” in the BPM field.

The biggest misnomer actually comes from the name itself — that is, that “big data” is about big data. When we talk about big data, we must put its size in relation to the available resources, the question asked, and the kind of data.

On the Path from Insight to Action (Analysis) —— Sparsity Vs. Redundancy

The widespread use of traditional data mining and artificial intelligence algorithms has usually exposed their limitations on data sparsity in large-scale data set or problems associated with high dimensionality. However, the large amount of process data always exhibits redundancy instead of sparsity.

For example, user-based collaborative filtering

systems have been very successful in the past,

but their weakness has been revealed for large,

sparse databases

Vs.

To deal with more than 8000 OA processes, the

technology to reduce duplications and make the

differences between process models explicit is

really important.

Identification of highly reusable fragments

approval process of province A

approval process of province B

reusable fragments (Draft & Approval)

Source: Analysis of Recommendation Algorithms for E-Commerce, Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl

On the Path from Insight to Action (Analysis) —— Sample Vs. Population

Take the process mining scenario for example, where the completeness of event log plays an extremely important role.

For limited event log (also recognized as sample), the global completeness needs to be evaluated by resorting to distribution fitting or at least bound estimation.

Sample based analysis is usually conducted to infer the whole behavior of population. However, in the age of big data, one turns to put emphasis on population but not sample, since collecting and processing large amount of data are feasible now.

For complete event log (also recognized as population), it seems the global completeness is definitely guaranteed. However, the data quality can also affect the efficiency of mining algorithms, while it suffers from data missing and noise infection for population data.

Therefore, one must pay more attention to unprecedented challenges when population based analysis was implemented, such as noise cancellation, redundancy removal, data quality improvement and so on.

Vs.

Arbitrary Jump

On the Path from Insight to Action (Analysis) —— Individual Vs. Network

Source: From Network Mining to Large Scale Business Networks, Daniel Ritter, WWW 2012 – LSNA‘12 Workshop, 2012

Sample of Cross Enterprise Business Network software

system

(process)

model

event

logs

models

analyzes

discovery

records

events, e.g.,

messages,

transactions,

etc.

specifies

configures

implements

analyzes

supports/

controls

enhancement

conformance

“world”

people machines

organizations

components

business

processes

applicationssoftware stack

network

modelraw data

models

real world,

analyzes

discovery

manages,e.g.,c

onfiguration,me

ssages,process

esspecifies

re-deploys

interpretes

analyzes

manages/

controls

enhancement

conformance

“enterprise”

people

machines

middleware

components

Business processes

Big data is often associated with complex data network, so that it is offering a fresh perspective, rapidly developing into a new network science discipline. It also has exerted a subtle influence on the BPM research and applications

From a business process network point of view, process mining is re-defined as the discipline that covers the discovery, extraction and domain specific analysis of relevant data from dynamic, distributed and heterogeneous enterprise landscapes.

On the Path from Insight to Action (Analysis) —— Causality Vs. Correlation

A major issue of concern in big data research is that correlation plays much more important role than causality. Google's founding philosophy is that we don't know why this page is better than that one: If the statistics of incoming links say it is, that's good enough. No semantic or causal analysis is required. However, we would like to mention that causality and correlation are equally important in BPM field.

Process mining is strongly based on the rigorous deduction of activity causality from event log.

Similarity analysis and clustering based technologies are typical correlation taken into consideration.

Clustering

Similarity

Background

From BPM to iBPM



Outline



• OA Processes Consolidation

• Social Network Analysis

• Intelligent Recommendation & Matching

• Test & Application of Distributed Cloud Storage

Outline

OA Process Consolidation Procedure

Fragment Repository

Raw Process Repository

Fragmentatization Tool

Process mining Tool

Process Template

Repository

Fragment Analysis

Tool

Process designer

BPMN 2.0 Specification

Temporary Fragment

Reopsitory

Legacy Process Asset

XLS Event Log

Process models

Raw process level process fragment level Refined process level

I. Mining Processes with Particular Business Logic

Algorithms: improved alpha++ Process Modeling Language: standard BPMN 2.0 Implementation: tailor made tool (to be shown in the video)

II. Fragmentization and Reuse

Algorithms: clone detection based on RPST Process Modeling Language: standard BPMN 2.0 Implementation: tailor made tool (to be shown in the video)

VI. Ontology based Rule Modeling

Process Modeling Language: standard BPMN 2.0, OWL Implementation: tailor made tool (to be shown in the video) External Tools: protégé 4.1

business understanding & raw process reconstruction

redundancy removal & process repository

flexible modeling based on business semantics

Complex business logic & recessive rules

IV. Merging

Algorithms: based on SPL (Software Product Line) Process Modeling Language: standard BPMN 2.0 Implementation: tailor made tool (to be shown in the video)

III. Similarity & Clustering

Algorithms: clone detection, hierarchical clustering, SSDT-matrix based behavioral similarity Process Modeling Language: standard BPMN 2.0, Newick format Implementation: tailor made tool (to be shown in the video) External Tools: Figtree, bpcd

V. Differentiation based on change operations

Process Modeling Language: standard BPMN 2.0 Implementation: tailor made tool (to be shown in the video)

Fragment Repository



Process mining Tool

Process Template

Repository

Fragment Analysis

Tool

Process designer


Temporary Fragment

Reopsitory


XLS Event Log

Process models


II. Fragmentization and Reuse

Source: Vanhatalo J, Völzer H, Koehler J. The refined process structure tree[M]//Business Process Management. Springer Berlin Heidelberg, 2008: 100-115.

Motivation

Fragments reuse can reduce duplications between processes.

Fragment based process modeling significantly improves the process design efficiency. By testing, the average duration for modeling a process can be reduced by 20% to 60%, when compared to the common approach without fragments

Fragment with specific business semantic can assist business analysts directly design processes with least IT efforts.

Based on clone detection and RPST, a procedure is proposed for process model decomposition, and thus, obtains highly reusable fragments.

Decompose process models into fragments that are suitable for reuse.

Select fragments that are frequently used. The selection can be made manually by experienced business analysts or automatically by thresholds (e.g., size of fragments), according to specific scenario.

Fragmentization of “sending document” process model

Uba R, Dumas M, García-Bañuelos L, et al. Clone detection in repositories of business process models[M]//Business Process Management. Springer Berlin Heidelberg, 2011: 248-264. Gao X., Chen Y., Ding Z., et.al, Process Model Fragmentization, Clustering and Merging: An Empirical Study

IV. Merging

Fragment Repository



Process mining Tool

Process Template

Repository

Fragment Analysis

Tool

Process designer


Temporary Fragment

Reopsitory


XLS Event Log

Process models


The merging operation converts a set of similar fragments into one merged fragment (also referred as master fragment), by borrowing some basic ideas from the software product line (SPL).

Variation points in the merged diagrams represent locations where the input models disagree in their behavior. That is, a variation point occurs when several alternative flows that belong to different input processes go out from a common activity.

Action

Blocking

Activating

HidingBlocking

Blocking and hiding are the essential concepts of configuration.

Configurable Process Model

Subtraction

Addition: Process Fragment Weaving

Fragment Repository



Process mining Tool

Process Template

Repository

Fragment Analysis

Tool

Process designer


Temporary Fragment

Reopsitory


XLS Event Log

Process models


V. Differentiation based on Change Operations

identifying change operations between models

Match nodes between two models (one to one match).

Delete nodes from the original model. These nodes do not have a matching node in the target model.

Objectives

Always get a sound model from a sound model by change operations. The correctness of OA process models/fragments changing and maintenance can be naturally guaranteed

More syntactical meanings and much less than change primitives. Make sure that changing operation can be structured recorded.

Model A

Model B

Compute the (minimal) movements. These movements that are operated on the original model, to match the relationship between matching nodes of models.

Insert nodes into the original model (at the right place). These nodes are in the target model but do not have a matching node in the original model.

Model A1

Model A2

typeset Archive

typeset Archive

Model A3=Model B

typeset Archive

print

1. Delete (A, Typeset) 2. Delete (A, Archive) 3. Move (A, Assign ID, Check Document,

Distribution 4. Insert (A, print, Distribute, end)

Source: Li C, Reichert M, Wombacher A. On measuring process model similarity based on high-level change operations[M]//Conceptual Modeling-ER 2008. Springer Berlin Heidelberg, 2008: 248-264.

Fragment Repository



Process mining Tool

Process Template

Repository

Fragment Analysis

Tool

Process designer


Temporary Fragment

Reopsitory


XLS Event Log

Process models


VI. Ontology based Rule Modeling

Motivation

Business processes are always influenced by legal and regulatory constraints according to managerial requirements. These kinds of constraints are always recessive.

Ontology based rule modeling can avoid redundancy and keep consistency

Ontology based rule modeling can make information sharable and exchangeable

Implementation

ontology is designed to allow modeling of external and internal regulations as guidelines and constraints on the interaction between entities and on states of process template.

Standard specification of a given language is provided as a descriptive facility and OWL is adopted to describe business rules.

Prototype Show

In the context of OA consolidation, a specialized configuration tool has been built, which integrates all the aforementioned algorithms as well as tools, aiming at providing flexible and efficient process modeling capability for both the business analysts and IT staffs.

Integrative modeling tool

User friendly interface

Open source based

Pluggable modules

intelligent analysis

Complete statistics

Snapshot of the fragment based process configuration tool

A short video




• Intelligent Recommendation


Enterprise Social Network Analysis (continued)

We have gathered about 20 million user and 1.2 billion microblogs. By using our SNA models, we achieved useful tools for enterprise management, such as public opinion tracing, VIP discovery, satisfaction survey and novel CRM.

Mining public topics and opinions from one billion micro-blogs

Method: Text Mining, Spam-trim,TDT,

Public Opinion Tracing

Opinion leader and VIP discovery

Satisfaction survey

Novel CRM

National-wide, 100 thousand user satisfaction survey

Method: Sentiment analysis, NLP

Discovery 244 opinion leader and VIPs out of 100 thousand users.

Method: Graph Clustering, Spam detection

Based on user ‘s satisfaction and his influence, we draw a user-care map

Method: Sentiment analysis, NLP, Social influence

Background and Targets

• Daily generated log data is up to

50GB now , resulting to a more than

20TB annual accumulation

• However, the data growth speed is

still rising

More than 200,000,000 users

More than 150,000 apps

More than 1,500,000 audios

More than 1,800,000 videos

More than 50,000 books

More than 60,000 comics

More than 100,000,000 PVs per day

More than 10,000,000 downloads per day

Truly massive data Severe information overload

Poor download rate

Very hard for users to choose items of their

interest

Very hard for developers to promote

products effectively

Targets of Intelligent Recommendation

To Realize tripartite win of the users,

developers and operators of MM

• For users: better user experience

• For developers: equitable opportunity to

promote products efficiently

• For operators: improving operational income

MM (Mobile Market) of CMCC is similar to Apple App Store, which provides not only more than 150,000 mobile apps, but also a huge number of other digital commodities. “Information overload“ problem of MM is becoming more and more challenging.

System Architecture

Run Hadoop Mapreduce on top of MongoDB cluster, achieving better performance

Complete all offline computing based on 12 months data in 5 hours

Every recommendation costs less than 10ms under concurrent PV up to 3000 per second

Data Storage

MM

Business Sys User

Intelligent Rec Engine

MM Log Sys

MM

UI

Results Optimization

ETL & Modeling Intelligent

Recommendation

System

Architecture

Hybrid CF

ALS-LFM

P-FP

Pluggable recommendation algorithms

Support customized algorithm parameter sets for different scenes

Three algorithms integrated now: Hybrid-CF(Hybrid Collaborative Filtering)、P-FP(Association rule mining)、ALS-LFM

We built the intelligent recommendation system for MM with open source big data tools, such as Hadoop, MongoDB, Mahout, etc.

Feature & Performance

Reciprocal Recommendation

Scenario 1 : Online Dating Scenario 2 : Social Networking

Scenario 3 : Job-Hunting Scenario 4 : Stable Roommates

Dealing with SMP（Stable Marriage Problem），to find a stable pair between two sets of elements representing men and women. Matching models such as Gale–Shapley algorithm are applied.

Recommending people on social networking sites. To help people to create social and personal connections, to expand friend lists. IBM Beehive using “Content-plus-Link” model to recommend new colleagues.

Known as Roommate Finder, Roommate matching Networks. To help students to find their satisfied roommates. Similar to the stable marriage problem, but differs in that all participants belong to a single pool.

Motivated by the matching between medical students and hospitals in the US, currently known as NRMP (National Resident Matching Program). A bilateral recommendation approach in matching people and jobs.

Traditional recommenders is to provide a USER with recommendations of ITEMS likely to be of interest to the user, such as books, movies, mobile APPs and pharmacy products. Apart from this, we also focus on another important class of recommendations named Reciprocal Recommender, where both the USER and the ITEM models represent people. The two sides have similar standing and both have preferences to be satisfied.

Test terms Test content MySQL Cassandra Mongo

DB HBase

Basic function

Data definition, data operation, control, manage & maintain, function and interface

Basic performance

System performance with full-read

System performance with full plug-in

System performance with upgrade

System performance with frequent read

System performance with frequent update

elastic System expansion with stable frequent read

System expansion with stable frequent update

flexibility ability of dynamic node extension with online service

High availability

group influence with failed manage node

Group influence with failed data node

Group influence with failed router node

Group influence with failed distributed node

Consistency group node synchronies time, estimate system consistency

Metrics and Evaluation of NoSQL Database

CM-CBF aims to test the suitability of cloud storages for different scenarios, in order to benchmark the performance, CM-CBF includes six aspects: basic function test, basic performance test, elastic test, flexibility test, high availability test, consistency test.

Background

From BPM to iBPM



Outline


The best way to predict the future is to create it. ——Peter F. Drucker

Conclusions and Future Work

social network analysis

machine learning

semantic web

data visualization

distributed cloud storage

Open R & D Ecosystem

Platform & Commercialization

Multi-discipline collaboration

build cloud enabled iBPM platform and provide on-demand analytical service

integrate various advanced technologies and tools into process engine and accelerate the evolution to iBPM

build open and harmony environments for both academia and industry

promote and encourage open source tools and prototypes for technology innovation and incubation

We sincerely hope to promote the relationship with academia, share our idea, devote ourselves to the advanced researches as well as their realization.

Thanks for your attentions

Towards the Next Generation Intelligent BPM

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