Click here to close now.




















Welcome!

@CloudExpo Authors: Liz McMillan, Dana Gardner, Pat Romanski, Richard Napolitano, Aruna Ravichandran

Related Topics: Microservices Expo, Java IoT, Industrial IoT, Microsoft Cloud, IoT User Interface, Apache

Microservices Expo: Article

Intelligent Complex Event Processing with Artificial Neural Network

Solve highly complex problems in real or near real time

In the current world, data is continuously being generated across various layers of organizations and environment due to changes in the system states or due to the occurrence of new events. These changes in the state of the existing system can happen due to the arrival of a new order request, customer service calls for complaints or feedback, changes in the company stock prices, text or multimedia messages, emails, social media posts, traffic reports, weather reports or any other kind of data. Simply producing reports using these data on a pre-defined schedule is not enough. Decision makers need real-time alerts and intelligent insight of all that is happening within and around the organization so that they may take meaningful reactive and proactive action before it is too late based on the new information being continuously generated.

A powerful technique called Complex Event Processing (CEP) is used for analyzing events coming from multiple sources over a specific period of time by detecting complex patterns between events and by making correlations. Apart from CEP, Artificial Neural Network (ANN) is also used to model complex relationships between input events data. Both the approaches have their own pros and cons. In this article, we tried to describe a use case in the health care domain with the solution architecture using both CEP and ANN, combining the best capabilities of both the approaches. We have shown how one can use both the techniques together to solve highly complex problems in real or near real time.

The following two sections gives brief introduction about CEP and ANN respectively with their key benefits. In section 4, we have explained the approach which combines both the CEP and the ANN efficiently to provide better solution of complex problems. Section 5 and 6 explains the Health Care: Patient Monitoring System use case with the problem description and proposed solution approach using CEP and ANN, followed by the section with summary and conclusion.

Complex Event Processing
Complex event processing is one of the key Operational Intelligence technology used to process one or more stream of data and information (also known as events) and deriving a meaningful conclusion using them. It allows one to set the request for an analysis or some query and then have it continuously executed and evaluated over time against one or many streams of events in a highly efficient manner. CEP is all about processing events that combines data from many sources to infer events or patterns that suggest more complicated circumstances [1].  For example, CEP can be used as Fraud Detection system, to detect suspicious credit card usage by monitoring credit card activity in real time and relating the current transactions with the historical data about a particular customer. The historical data which can be used by CEP Fraud Detection system can be an average transaction amount, minimum and maximum values of the previous transactions, transaction frequencies, locality etc. On detecting fraudulent activity, CEP system can send an alert via an SMS or email to the customer or the credit card service provider to take quick reaction.

The primary goal of CEP is to (1) detect meaningful events or pattern of events which signifies either threats or opportunities from the series of events being received continuously and (2) send alerts for the same to responsible entity to respond as quickly as possible. The following diagram (as figure-1) describes high level view of the CEP system.

Figure 1: High-level view of the CEP system

As shown in Figure 1, the core of the complex event processing system is made up of set of input adapters, set of output adapters and various event processing modules such as event filtering modules, in-memory caching, aggregation over different windows (time-window, sliding window, tumbling window etc.), database lookups module, database writes module, correlation, joins, event pattern matching, state machines, dynamic queries etc. More the number of I/O adapters supported by the CEP, more flexible and adaptable it is and will be able to cover wide range of use cases as compared to the CEP tool having support for limited set of I/O adapters.

Key Benefits of CEP
The following are some of the key benefits the CEP provides to the business.

  • Automatically identifies rare but important relationships between seemingly unrelated events or stream of events and accelerate timely responses to both the threats and opportunities.
  • Using sophisticated analysis and event pattern matching techniques, the CEP improves resource allocation and timely problem resolution by prioritize situations that require the most urgent attention in real or near real time based on arrival of events.
  • CEP helps organization to reduce operating costs by monitoring end-to-end performance of the system and provide timely alerts to rapidly identify potential SLA violations.
  • CEP helps organization to fine tune their business processes by correlating SLA performance with industry metrics e.g. Six Sigma and various Quality metrics, to enhance overall productivity.

Artificial Neural Network
An Artificial Neural Network (ANN) is a computational model which resembles with the way human brain is made up of in structure and the way it works. Similar to human brain which is made up of billions of neurons interconnected by synapses, the ANN can be form as a network of computational nodes connected with each other through links. The ANN needs to be trained repeatedly with specific set of training data before it can be used in production environment. Due to its adaptive nature, the internal structure of the ANN can easily be changed based on external or internal information that flows through the network during the learning phase [2]. The links are assigned weights during training process, which regulate the flow of data from one node to another. ANNs are used to model complex relationships between inputs and outputs data. ANN can efficiently find various patterns in input data or to predict future values of the system parameters. Due to its flexible construct, ANN can be very helpful in modeling complex systems which are very difficult otherwise by using traditional modeling techniques. Artificial neural networks are being applied in diverse of domains and fields. They are extensively used for doing image processing and recognition, speech recognition, credit card fraud detection, for prediction of protein structure in biotechnology and in the field of genetic science.

Artificial neural network consists of two types of interfaces with the external world, the input and the output. Since the ANN is made up of nodes or neurons and the links between them, a subset of total nodes in the ANN act as input nodes, which take data from the external world, a subset of nodes act as output node, which produces result and zero or more hidden nodes act as intermediary nodes, with having only connections with input or output nodes or other hidden nodes.  Hence, the ANN is made up of nodes in input layer, nodes in output layer and zero or more internal layers.

Figure 2: High-level view of artificial neural network

The high level view of ANN is shown in figure-2. The diagram shows a typical neural network with total 12 nodes, three nodes in the input layer, seven nodes in the hidden layer and two nodes in the output layer. Before the neural network can be used in actual production environment, it is needed to be trained for particular environment. The process of training of ANN is called learning of neural network, which is generally done in one of the following three ways:  (a) supervised learning; (b) unsupervised learning and (c) reinforcement learning. The more details about the ANN learning can be found in [2].

Key Benefits of ANN
Since ANNs can infer a function from inputs, they particularly are used in the applications where the complexity of the input data or system modeling makes the design of such a function impractical using traditional approaches. Following are some of the key benefits ANN provides.

  • It is very easy to apply ANN to problem domains where the relationships are quite dynamic or non-linear among the input and output.
  • Since ANN is capable of capturing many kind of relationships and complex patterns among data, ANN allows user to easily model the system which otherwise is very difficult or impossible to represent through traditional modeling approaches.
  • The training information is not stored in any single element but is distributed in the entire network structure. This makes ANN fault tolerant and it reduces the impact of erroneous input on the result.

CEP and ANN Together
Having seen the key properties and benefits of using both, CEP and ANN, this section describes what if one apply both together for specific set of problems to make the modeling of the system and solution easy and efficient. The CEP is best in accepting data or events from multiple channels and apply various event processing operations on it, such as event filtering, event pattern matching, aggregation etc. Apart from that user can configure alerts based on various thresholds on various system parameters. But the CEP tools lakes the ability to predict future events or determine the values of the system parameters for future events, which can be efficiently done by the ANN. So if we combine best of CEP and best of ANN for a particular problem, the resulting solution could be very effective and efficient. In the following sections, we have described how the CEP and the ANN can be used together to solve a particular problem of patient monitoring system in the domain of Health care and medicines.

Patient Monitoring System
The patient monitoring system monitors and keeps track of various body parameters of the patient and provides the data for analysis to monitoring system. Various body parameters could be blood pressure, the percentage of oxygen in the blood, glucose level in the blood, heart beat rate, change in body temperature etc. Data provided by the patient monitoring system helps to make diagnostic decisions easy and more reliable. The quality of patient treatment and care giving can greatly be improved with the use of patient monitoring systems, since it allows generating alerts in case of sudden changes in the patient body parameters which could be dangerous to the patient's health or could be life threatening some time [3].

A Use Case
Goals of the patient monitoring system are to (1) continuously keeps track of the patient's body parameters and store the data for present or future references, (2) identify life-threatening changes in patient's body and raises timely alarms for the same, and (3) to determine whether patient's health is in normal condition or it is improving or worsening based on the continuously arriving input data from various medical monitors. Since no two human bodies react in a same way against given situation or medication, it is very difficult to derived common rule set which can be applied to all human bodies. Similarly, one person's body also reacts differently in different medical and environmental situations. For example, a particular heart beat rate can be normal in some situation, while the same can be very abnormal in the other situation. So to judge the proper health condition, a trained professional is required, i.e. a specialist doctor, who studies all the observations and determine the correct state of patient's health. If the patient monitoring system is equipped with some intelligent agent who will use patient's medical history and current body parameters observations, then quality of patient care delivery can greatly be improved. We combine CEP and ANN together to propose system architecture which tries to act as an intelligent agent of the patient monitoring system, which is described in the following section.

System Architecture of the intelligent patient monitoring system using CEP and ANN
The following diagram, in Figure 3, shows the architecture of the intelligent patient monitoring system using CEP and ANN. There are total five key components; (1) Medical monitors, (2) CEP, (3) Patient's medical history and diagnosis data store, (4) ANN and (5) ANN output to action message converter.

(1) Medical Monitors
Medical monitors are medical devices used for monitoring patient's body parameters. It can consist of one or more body parameter sensors, processing components, display devices as well as communication links for displaying, recording or transmitting data or results elsewhere through a monitoring network. In the proposed architecture, the data generated by medical monitors are fed into the CEP system. [3]

Figure 3: Architecture of the intelligent patient monitoring system using CEP and ANN

The CEP section of the proposed architecture is one of the key components of the system. It receives all the monitored data and applies various event processing techniques, such as filtering, aggregation etc. over input event streams and provides the data for further processing to ANN module. Various input adapters available in CEP make it possible to collect data from different types of sensors or monitors and process them collectively. In CEP module, various event processing rule are written specific to the patient.

(3) Patient's medical history and diagnosis data store
This is the data store where patient's medical history and diagnosis data is stored. It could be traditional RDBMS storage system. The data stored in this storage are used for ANN training purpose. The new data is continuously added into the same data storage and will be used next time when ANN will be trained again with patient's latest medical and diagnosis data.

(4) ANN
The ANN model for the patient is computational neural network specific to the patient and trained using patient's all medical and diagnosis data. This trained ANN model is used for real-time diagnosis and care delivery. The decision is taken based on the input data coming from the CEP output adapters. The patient specific ANN model is trained at regular interval may be daily or on need bases. These regular updates which include latest knowledge about measured body parameters, diagnosis and medication information of the patient, helps ANN model to make accurate predictions. It is also possible to make ANN take biased decision by giving more weight to either historical data or the latest data during training. All these make ANN the most critical component of the system.

(5) ANN output to action message converter
The output generated by the ANN is generally real numbers and they are needed to be mapped to the meaningful information so that appropriate action can be taken. This is done by the ANN output to action message converter. The module not only map ANN output to real world information but it can also sends action data or alerts to devices or human being through email, SMS, alarm system etc. The threshold for various alerts can be configured so it can adapt to the changes happening to the health and body.

Together all these components make a very flexible, intelligent and efficient patient monitoring system. The proposed architecture shows how one can use CEP and ANN together more effectively to model the complex problem and provide efficient solution alternative over the traditional approaches.

Conclusion
Complex event processing and artificial neural network are the two widely used solution techniques for the problems that are very difficult to model using traditional approaches. In this article, we have described both the approaches in brief with their key capabilities. We have also described a use case for intelligent patient monitoring system with the solution architecture using both CEP and ANN and combining the best capabilities of both the approaches. We have shown how one can use both the techniques together to solve highly complex problems in real or near real time.

References

  1. Complex event processing, http://en.wikipedia.org/wiki/Complex_event_processing#cite_note-1
  2. Artificial neural network, http://en.wikipedia.org/wiki/Artificial_neural_network
  3. Patient Monitoring Systems - Part 1, http://www.philblock.info/hitkb/p/patient_monitoring_systems.html

More Stories By Kamalkumar Mistry

Kamalkumar Mistry is a Technology Analyst at Infosys Limited, Pune, India. At Infosys, he is part of a research group called Infosys Labs (http://www.infosys.com/infosys-labs).

Comments (0)

Share your thoughts on this story.

Add your comment
You must be signed in to add a comment. Sign-in | Register

In accordance with our Comment Policy, we encourage comments that are on topic, relevant and to-the-point. We will remove comments that include profanity, personal attacks, racial slurs, threats of violence, or other inappropriate material that violates our Terms and Conditions, and will block users who make repeated violations. We ask all readers to expect diversity of opinion and to treat one another with dignity and respect.


@CloudExpo Stories
The speed of software changes in growing and large scale rapid-paced DevOps environments presents a challenge for continuous testing. Many organizations struggle to get this right. Practices that work for small scale continuous testing may not be sufficient as the requirements grow. In his session at DevOps Summit, Marc Hornbeek, Sr. Solutions Architect of DevOps continuous test solutions at Spirent Communications, explained the best practices of continuous testing at high scale, which is rele...
"We got started as search consultants. On the services side of the business we have help organizations save time and save money when they hit issues that everyone more or less hits when their data grows," noted Otis Gospodnetić, Founder of Sematext, in this SYS-CON.tv interview at @DevOpsSummit, held June 9-11, 2015, at the Javits Center in New York City.
"We have been in business for 21 years and have been building many enterprise solutions, all IT plumbing - server, storage, interconnects," stated Alex Gorbachev, President of Intelligent Systems Services, in this SYS-CON.tv interview at 16th Cloud Expo, held June 9-11, 2015, at the Javits Center in New York City.
"We specialize in testing. DevOps is all about continuous delivery and accelerating the delivery pipeline and there is no continuous delivery without testing," noted Marc Hornbeek, Sr. Solutions Architect at Spirent Communications, in this SYS-CON.tv interview at @DevOpsSummit, held June 9-11, 2015, at the Javits Center in New York City.
"Alert Logic is a managed security service provider that basically deploys technologies, but we support those technologies with the people and process behind it," stated Stephen Coty, Chief Security Evangelist at Alert Logic, in this SYS-CON.tv interview at 16th Cloud Expo, held June 9-11, 2015, at the Javits Center in New York City.
Digital Transformation is the ultimate goal of cloud computing and related initiatives. The phrase is certainly not a precise one, and as subject to hand-waving and distortion as any high-falutin' terminology in the world of information technology. Yet it is an excellent choice of words to describe what enterprise IT—and by extension, organizations in general—should be working to achieve. Digital Transformation means: handling all the data types being found and created in the organizat...
The essence of cloud computing is that all consumable IT resources are delivered as services. In his session at 15th Cloud Expo, Yung Chou, Technology Evangelist at Microsoft, demonstrated the concepts and implementations of two important cloud computing deliveries: Infrastructure as a Service (IaaS) and Platform as a Service (PaaS). He discussed from business and technical viewpoints what exactly they are, why we care, how they are different and in what ways, and the strategies for IT to tran...
The Internet of Everything (IoE) brings together people, process, data and things to make networked connections more relevant and valuable than ever before – transforming information into knowledge and knowledge into wisdom. IoE creates new capabilities, richer experiences, and unprecedented opportunities to improve business and government operations, decision making and mission support capabilities.
The Software Defined Data Center (SDDC), which enables organizations to seamlessly run in a hybrid cloud model (public + private cloud), is here to stay. IDC estimates that the software-defined networking market will be valued at $3.7 billion by 2016. Security is a key component and benefit of the SDDC, and offers an opportunity to build security 'from the ground up' and weave it into the environment from day one. In his session at 16th Cloud Expo, Reuven Harrison, CTO and Co-Founder of Tufin,...
The Internet of Things is not only adding billions of sensors and billions of terabytes to the Internet. It is also forcing a fundamental change in the way we envision Information Technology. For the first time, more data is being created by devices at the edge of the Internet rather than from centralized systems. What does this mean for today's IT professional? In this Power Panel at @ThingsExpo, moderated by Conference Chair Roger Strukhoff, panelists addressed this very serious issue of pro...
With SaaS use rampant across organizations, how can IT departments track company data and maintain security? More and more departments are commissioning their own solutions and bypassing IT. A cloud environment is amorphous and powerful, allowing you to set up solutions for all of your user needs: document sharing and collaboration, mobile access, e-mail, even industry-specific applications. In his session at 16th Cloud Expo, Shawn Mills, President and a founder of Green House Data, discussed h...
Container technology is sending shock waves through the world of cloud computing. Heralded as the 'next big thing,' containers provide software owners a consistent way to package their software and dependencies while infrastructure operators benefit from a standard way to deploy and run them. Containers present new challenges for tracking usage due to their dynamic nature. They can also be deployed to bare metal, virtual machines and various cloud platforms. How do software owners track the usag...
Discussions about cloud computing are evolving into discussions about enterprise IT in general. As enterprises increasingly migrate toward their own unique clouds, new issues such as the use of containers and microservices emerge to keep things interesting. In this Power Panel at 16th Cloud Expo, moderated by Conference Chair Roger Strukhoff, panelists addressed the state of cloud computing today, and what enterprise IT professionals need to know about how the latest topics and trends affect t...
"Our biggest growth area has been the security services, the managed services - the things that differentiate us in the market that there is no client that's too small and there's no client that's too big," explained Paul Mazzucco, Chief Security Officer at TierPoint, in this SYS-CON.tv interview at 16th Cloud Expo, held June 9-11, 2015, at the Javits Center in New York City.
SYS-CON Events announced today that HPM Networks will exhibit at the 17th International Cloud Expo®, which will take place on November 3–5, 2015, at the Santa Clara Convention Center in Santa Clara, CA. For 20 years, HPM Networks has been integrating technology solutions that solve complex business challenges. HPM Networks has designed solutions for both SMB and enterprise customers throughout the San Francisco Bay Area.
Containers are changing the security landscape for software development and deployment. As with any security solutions, security approaches that work for developers, operations personnel and security professionals is a requirement. In his session at DevOps Summit, Kevin Gilpin, CTO and Co-Founder of Conjur, will discuss various security considerations for container-based infrastructure and related DevOps workflows.
Countless business models have spawned from the IaaS industry. Resell Web hosting, blogs, public cloud, and on and on. With the overwhelming amount of tools available to us, it's sometimes easy to overlook that many of them are just new skins of resources we've had for a long time. In his General Session at 16th Cloud Expo, Phil Jackson, Lead Technology Evangelist at SoftLayer, broke down what we've got to work with and discuss the benefits and pitfalls to discover how we can best use them to d...
"We do data integration for B2B also application to application, and we do data management and enable Big Data," explained Pat Adamiak, Vice President, Product Marketing at Liaison Technologies, in this SYS-CON.tv interview at 16th Cloud Expo, held June 9-11, 2015, at the Javits Center in New York City.
Chuck Piluso presented a study of cloud adoption trends and the power and flexibility of IBM Power and Pureflex cloud solutions. Prior to Secure Infrastructure and Services, Mr. Piluso founded North American Telecommunication Corporation, a facilities-based Competitive Local Exchange Carrier licensed by the Public Service Commission in 10 states, serving as the company's chairman and president from 1997 to 2000. Between 1990 and 1997, Mr. Piluso served as chairman & founder of International Te...
The Cloud industry has moved from being more than just being able to provide infrastructure and management services on the Cloud. Enter a new era of Cloud computing where monetization’s services through the Cloud are an essential piece of strategy to feed your organizations bottom-line, your revenue and Profitability. In their session at 16th Cloud Expo, Ermanno Bonifazi, CEO & Founder of Solgenia, and Ian Khan, Global Strategic Positioning & Brand Manager at Solgenia, discussed how to easily o...