Welcome!

Cloud Expo Authors: Liz McMillan, Elizabeth White, Cloud Ventures, Pat Romanski, Michelle Drolet

Related Topics: SOA & WOA, Java, XML, .NET, AJAX & REA, Apache

SOA & WOA: 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.


Cloud Expo Breaking News
More and more enterprises today are doing business by opening up their data and applications through APIs. Though forward-thinking and strategic, exposing APIs also increases the surface area for potential attack by hackers. To benefit from APIs while staying secure, enterprises and security architects need to continue to develop a deep understanding about API security and how it differs from traditional web application security or mobile application security. In his session at 14th Cloud Expo, Sachin Agarwal, VP of Product Marketing and Strategy at SOA Software, will walk you through the various aspects of how an API could be potentially exploited. He will discuss the necessary best practices to secure your data and enterprise applications while continue continuing to support your business’s digital initiatives.
The revolution that happened in the server universe over the past 15 years has resulted in an eco-system that is more open, more democratically innovative and produced better results in technically challenging dimensions like scale. The underpinnings of the revolution were common hardware, standards based APIs (ex. POSIX) and a strict adherence to layering and isolation between applications, daemons and kernel drivers/modules which allowed multiple types of development happen in parallel without hindering others. Put simply, today's server model is built on a consistent x86 platform with few surprises in its core components. A kernel abstracts away the platform, so that applications and daemons are decoupled from the hardware. In contrast, networking equipment is still stuck in the mainframe era. Today, networking equipment is a single appliance, including hardware, OS, applications and user interface come as a monolithic entity from a single vendor. Switching between different vendor'...
You use an agile process; your goal is to make your organization more agile. What about your data infrastructure? The truth is, today’s databases are anything but agile – they are effectively static repositories that are cumbersome to work with, difficult to change, and cannot keep pace with application demands. Performance suffers as a result, and it takes far longer than it should to deliver on new features and capabilities needed to make your organization competitive. As your application and business needs change, data repositories and structures get outmoded rapidly, resulting in increased work for application developers and slow performance for end users. Further, as data sizes grow into the Big Data realm, this problem is exacerbated and becomes even more difficult to address. A seemingly simple schema change can take hours (or more) to perform, and as requirements evolve the disconnect between existing data structures and actual needs diverge.
Cloud backup and recovery services are critical to safeguarding an organization’s data and ensuring business continuity when technical failures and outages occur. With so many choices, how do you find the right provider for your specific needs? In his session at 14th Cloud Expo, Daniel Jacobson, Technology Manager at BUMI, will outline the key factors including backup configurations, proactive monitoring, data restoration, disaster recovery drills, security, compliance and data center resources. Aside from the technical considerations, the secret sauce in identifying the best vendor is the level of focus, expertise and specialization of their engineering team and support group, and how they monitor your day-to-day backups, provide recommendations, and guide you through restores when necessary.
Cloud scalability and performance should be at the heart of every successful Internet venture. The infrastructure needs to be resilient, flexible, and fast – it’s best not to get caught thinking about architecture until the middle of an emergency, when it's too late. In his interactive, no-holds-barred session at 14th Cloud Expo, Phil Jackson, Development Community Advocate for SoftLayer, will dive into how to design and build-out the right cloud infrastructure.
SYS-CON Events announced today that SherWeb, a long-time leading provider of cloud services and Microsoft's 2013 World Hosting Partner of the Year, will exhibit at SYS-CON's 14th International Cloud Expo®, which will take place on June 10–12, 2014, at the Javits Center in New York City, New York. A worldwide hosted services leader ranking in the prestigious North American Deloitte Technology Fast 500TM, and Microsoft's 2013 World Hosting Partner of the Year, SherWeb provides competitive cloud solutions to businesses and partners around the world. Founded in 1998, SherWeb is a privately owned company headquartered in Quebec, Canada. Its service portfolio includes Microsoft Exchange, SharePoint, Lync, Dynamics CRM and more.
The world of cloud and application development is not just for the hardened developer these days. In their session at 14th Cloud Expo, Phil Jackson, Development Community Advocate for SoftLayer, and Harold Hannon, Sr. Software Architect at SoftLayer, will pull back the curtain of the architecture of a fun demo application purpose-built for the cloud. They will focus on demonstrating how they leveraged compute, storage, messaging, and other cloud elements hosted at SoftLayer to lower the effort and difficulty of putting together a useful application. This will be an active demonstration and review of simple command-line tools and resources, so don’t be afraid if you are not a seasoned developer.
SYS-CON Events announced today that BUMI, a premium managed service provider specializing in data backup and recovery, will exhibit at SYS-CON's 14th International Cloud Expo®, which will take place on June 10–12, 2014, at the Javits Center in New York City, New York. Manhattan-based BUMI (Backup My Info!) is a premium managed service provider specializing in data backup and recovery. Founded in 2002, the company’s Here, There and Everywhere data backup and recovery solutions are utilized by more than 500 businesses. BUMI clients include professional service organizations such as banking, financial, insurance, accounting, hedge funds and law firms. The company is known for its relentless passion for customer service and support, and has won numerous awards, including Customer Service Provider of the Year and 10 Best Companies to Work For.
Chief Security Officers (CSO), CIOs and IT Directors are all concerned with providing a secure environment from which their business can innovate and customers can safely consume without the fear of Distributed Denial of Service attacks. To be successful in today's hyper-connected world, the enterprise needs to leverage the capabilities of the web and be ready to innovate without fear of DDoS attacks, concerns about application security and other threats. Organizations face great risk from increasingly frequent and sophisticated attempts to render web properties unavailable, and steal intellectual property or personally identifiable information. Layered security best practices extend security beyond the data center, delivering DDoS protection and maintaining site performance in the face of fast-changing threats.
From data center to cloud to the network. In his session at 3rd SDDC Expo, Raul Martynek, CEO of Net Access, will identify the challenges facing both data center providers and enterprise IT as they relate to cross-platform automation. He will then provide insight into designing, building, securing and managing the technology as an integrated service offering. Topics covered include: High-density data center design Network (and SDN) integration and automation Cloud (and hosting) infrastructure considerations Monitoring and security Management approaches Self-service and automation
In his session at 14th Cloud Expo, David Holmes, Vice President at OutSystems, will demonstrate the immense power that lives at the intersection of mobile apps and cloud application platforms. Attendees will participate in a live demonstration – an enterprise mobile app will be built and changed before their eyes – on their own devices. David Holmes brings over 20 years of high-tech marketing leadership to OutSystems. Prior to joining OutSystems, he was VP of Global Marketing for Damballa, a leading provider of network security solutions. Previously, he was SVP of Global Marketing for Jacada where his branding and positioning expertise helped drive the company from start-up days to a $55 million initial public offering on Nasdaq.
Performance is the intersection of power, agility, control, and choice. If you value performance, and more specifically consistent performance, you need to look beyond simple virtualized compute. Many factors need to be considered to create a truly performant environment. In his General Session at 14th Cloud Expo, Marc Jones, Vice President of Product Innovation for SoftLayer, will explain how to take advantage of a multitude of compute options and platform features to make cloud the cornerstone of your online presence.
Are you interested in accelerating innovation, simplifying deployments, reducing complexity, and lowering development costs? The cloud is changing the face of application development and deployment, with enterprise-grade infrastructure and platform services making it possible for you to build and rapidly scale enterprise applications. In his session at 14th Cloud Expo, Gene Eun, Sr. Director, Oracle Cloud at Oracle, will discuss the latest solutions and strategies for application developers and enterprise IT organizations to leverage Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) to build and deploy modern business applications in the cloud.
Hybrid cloud refers to the federation of a public and private cloud environment for the purpose of extending the elastic and flexibility of compute, storage and network capabilities, in an on-demand, pay-as-you go basis. The hybrid approach allows a business to take advantage of the scalability and cost-effectiveness that a public cloud computing environment offers without exposing mission-critical applications and data to third-party vulnerabilities. Hybrid cloud environments involve complex management challenges. First, organizations struggle to maintain control over the resources that lie outside of their managed IT scope. They also need greater infrastructure visibility to help reduce maintenance costs and ensure that their company data and resources are properly handled and secured.
As more applications and services move "to the cloud" (public or on-premise), cloud environments are increasingly adopting and building out traditional enterprise features. This in turn is enabling and encouraging cloud adoption from enterprise users. In many ways the definition is blurring as features like continuous operation, geo-distribution or on-demand capacity become the norm. At NuoDB we're involved in both building enterprise software and using enterprise cloud capabilities. In his session at 14th Cloud Expo, Seth Proctor, CTO of NuoDB, Inc., will cover experiences from building, deploying and using enterprise services and suggest some ways to approach moving enterprise applications into a cloud model.