The History Of Complex Event Processing

Published Date: 02 Nov 2017

ABSTRACT

This paper is prepared to explain the EMERGING TECHNOLOGIES FOR BUSINESS INTELLIGENCE. ETFBI using different tools and technologies in the field of Business Intelligence (BI) including integration into business processes management improves decision making techniques. Research discussed in this paper addresses the information intelligence and describes technologies supportive for scalable data integration. The technologies mentioned in this paper are used for developing a strong Business Intelligence Environment.

KEYWORDS

BI: Business Intelligence, DW: Data Warehouse, DSS: Decision Support Systems, DM: Data Mining, KD: Knowledge Discovery, CE: Configuration Engine, ER: Enterprise Reporting, BR: Business Reporting.

INTRODUCTION

In today’s business environment organizations are generating huge amount of data, having large arrays of databases, regulated by business needs. Using this data to make business decisions is very difficult as abundant data is very complex to handle and process. Often it is unknown what insights are hidden in those databases. Software systems including BI have to be designed and developed to be flexible, customizable and scalable to cope with today’s business demands and frequently changing requirements.

Most BI systems processes data from centralized databases called Data warehouse. Vital information generated from these DWs helps businesses to make decision. Thus a BI system can be called a Decision Support Systems (DSS).

Common functions of BI technologies are prescriptive analytics, complex event processing, online analytical processing, analytics, predictive analytics, process mining, reporting, business performance management, data mining, benchmarking, text mining.

ETFBI EMERGING TECHNOLOGIES FOR BUSINESS INTELLIGENCE

ETFBI MODULE

Figure 1

DATA WAREHOUSE

Every decision unit has its own data storage or access (via

Web Service, TCP/IP, Named Pipes, etc.) to a

dedicated database or database server. (A relational

database might be the most common form of data

storage but others are possible), this data storage is

used to store) agent Meta data like the Agent ID and

other parameters) data about the problem and

problem domain (e.g. Pricing and Sales data)

feedback data (learning or experience data). The

DB/DW component is the agent equivalent to the

Agent’s Belief database / set.

DATA MINING/ KNOWLEDGE DISCOVERY

Knowledge Discovery (DM/KD). The DM/KD module in a DU has the role

of analyzing the problem data and delivering a result

that can be used in the decision execution module.

Methods used might vary depending on the problem

domain and environment; however Time Series

Analysis, Artificial Neural Networks and k-Nearest

Neighbor are methods that are frequently used in BI.

Complex systems might interact with specialized

software such as Math lab.

DECISION EXECUTION

In an approach which may differ from traditional BI/DSS systems, a DU in MABBI can implement the result of a decision into the business process at the local level. This means that as soon as new knowledge becomes available the organization can benefit and act upon it. It is not necessary that a user transfer the results from the BI system to the operational system, which can be a time consuming and error prone process.

LEARNING (FEEDBACK)

To improve the outcome

of the Decision Unit for future decisions a learning

component tracks the "real world" after a decision was

implemented and adjusts future decisions based on the

feedback if necessary. The learning module is closely

related to the DM/KD component to utilize different

methods and models. To give a DU the means to do

this, previous outcomes can be logged in the DB and

used as supplementary input.

COMMUNICATION

Flexibility is a key aspect of

the system that requires broad communication

facilities. In Figure 1 the communication layer around

the DU is easily recognizable. It indicates that all

modules in a DU can communicate (e.g. has access to

data sources / operational systems). If required, DU

can communicate with each other. These

communication capabilities can be realized in various

ways.

CONFIGURATION ENGINE

The Configuration Engine’s task is to

monitor systems, for example a DB and apply changes

to the structure of the agent system. The Configuration

Engine, which in turn is also an agent, is a helper in the

system where DUs cannot take required actions. For

example the CE executes the creation of a DU, as a DU

cannot create itself. Once a DU is created it accesses

suitable DBs to acquire data and information that is

necessary to adjust to the environment and structure

(e.g. the organizational hierarchy). After receiving the

start up parameters the DU acts on its own and

performs tasks according the design objectives. The

internal or local DB allows storing data that is of

relevance for the particular DU and the current context.

PREDECTIVE ANALYTICS

Predictive analytics BI models help finding pattern in historical and transactional data in order to identify risks and opportunities. Relationships are being captured among many factors to allow assessment of risk or guiding decision making for transactions that are candidates. Predictive analytics organized in a variety of techniques from modeling, machine learning, data mining and statistics that analyze historical and current facts to make future predictions.

Figure 2

COMPLEX EVENT PROCESSING

Complex Event processing is a technique of analyzing and tracking, processing streams of information (data) about events that happen, and deriving a conclusion from them. CEP is event processing that combines multiple sourced data to infer events or more complicated circumstances. The goal of complex event processing is to identify meaningful events (such as opportunities or threats) and respond to them as quickly as possible.

Figure 3

BUSINESS REPORTING

Enterprise Reporting or Business Reporting has a larger movement towards knowledge management and improved business intelligence. ER / BR implementation involves extract, transform, and load (ETL) procedures in coordination with a data warehouse and then using one or more reporting tools. Reports can be distributed via email or in print form typically accessed via a corporate intranet. The reporting process involves querying data sources with different logical models to produce a human readable report, for example, a computer user has to query the Human Resources databases and the Capital Improvements databases to show how efficiently space is being used across an entire corporation.

Figure 4

PROCESS MINING

Process mining is associated with BPI (Business Process Intelligence), BOM (Business Operations Management), BAM (Business Activity Monitoring) and workflow / data mining. Focus is on processes and questions that transcend the simple performance-related queries supported by tools such as Business Hyperion, Objects, Cognos BI.

CONCLUSION / FUTURE RESEARCH

This research proposed a new architecture, called

ETFBI that combines Business Intelligence with

Emerging Technology to deliver localized decision-

making capabilities throughout an organization.

Traditional business intelligence solutions use

centralized data warehouses and distribute the results to

local level if needed. Most applications will tend to use

a global view of the data for analysis. It is however

possible to consider environments in which local

knowledge is more valuable than a global assessment.

In pricing for example, issues such as local tastes and

socio-economic level will influence demand for

specific products which may differ from demand in

other locations. The research issue considered was

whether an agent-based architecture could deliver a

solution to such local decision making in a business

intelligence context i.e. learning from and analyzing

the local data.