Welcome!

XML Authors: Liz McMillan, Pat Romanski, Elizabeth White, Rick Delgado, Yeshim Deniz

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.


@ThingsExpo Stories
The Internet of Things is a misnomer. That implies that everything is on the Internet, and that simply should not be - especially for things that are blurring the line between medical devices that stimulate like a pacemaker and quantified self-sensors like a pedometer or pulse tracker. The mesh of things that we manage must be segmented into zones of trust for sensing data, transmitting data, receiving command and control administrative changes, and peer-to-peer mesh messaging. In his session at @ThingsExpo, Ryan Bagnulo, Solution Architect / Software Engineer at SOA Software, focused on desi...
"At our booth we are showing how to provide trust in the Internet of Things. Trust is where everything starts to become secure and trustworthy. Now with the scaling of the Internet of Things it becomes an interesting question – I've heard numbers from 200 billion devices next year up to a trillion in the next 10 to 15 years," explained Johannes Lintzen, Vice President of Sales at Utimaco, in this SYS-CON.tv interview at @ThingsExpo, held Nov 4–6, 2014, at the Santa Clara Convention Center in Santa Clara, CA.
"For over 25 years we have been working with a lot of enterprise customers and we have seen how companies create applications. And now that we have moved to cloud computing, mobile, social and the Internet of Things, we see that the market needs a new way of creating applications," stated Jesse Shiah, CEO, President and Co-Founder of AgilePoint Inc., in this SYS-CON.tv interview at 15th Cloud Expo, held Nov 4–6, 2014, at the Santa Clara Convention Center in Santa Clara, CA.
SYS-CON Events announced today that Gridstore™, the leader in hyper-converged infrastructure purpose-built to optimize Microsoft workloads, will exhibit at SYS-CON's 16th International Cloud Expo®, which will take place on June 9-11, 2015, at the Javits Center in New York City, NY. Gridstore™ is the leader in hyper-converged infrastructure purpose-built for Microsoft workloads and designed to accelerate applications in virtualized environments. Gridstore’s hyper-converged infrastructure is the industry’s first all flash version of HyperConverged Appliances that include both compute and storag...
"People are a lot more knowledgeable about APIs now. There are two types of people who work with APIs - IT people who want to use APIs for something internal and the product managers who want to do something outside APIs for people to connect to them," explained Roberto Medrano, Executive Vice President at SOA Software, in this SYS-CON.tv interview at Cloud Expo, held Nov 4–6, 2014, at the Santa Clara Convention Center in Santa Clara, CA.
Today’s enterprise is being driven by disruptive competitive and human capital requirements to provide enterprise application access through not only desktops, but also mobile devices. To retrofit existing programs across all these devices using traditional programming methods is very costly and time consuming – often prohibitively so. In his session at @ThingsExpo, Jesse Shiah, CEO, President, and Co-Founder of AgilePoint Inc., discussed how you can create applications that run on all mobile devices as well as laptops and desktops using a visual drag-and-drop application – and eForms-buildi...
We certainly live in interesting technological times. And no more interesting than the current competing IoT standards for connectivity. Various standards bodies, approaches, and ecosystems are vying for mindshare and positioning for a competitive edge. It is clear that when the dust settles, we will have new protocols, evolved protocols, that will change the way we interact with devices and infrastructure. We will also have evolved web protocols, like HTTP/2, that will be changing the very core of our infrastructures. At the same time, we have old approaches made new again like micro-services...
Code Halos - aka "digital fingerprints" - are the key organizing principle to understand a) how dumb things become smart and b) how to monetize this dynamic. In his session at @ThingsExpo, Robert Brown, AVP, Center for the Future of Work at Cognizant Technology Solutions, outlined research, analysis and recommendations from his recently published book on this phenomena on the way leading edge organizations like GE and Disney are unlocking the Internet of Things opportunity and what steps your organization should be taking to position itself for the next platform of digital competition.
The 3rd International Internet of @ThingsExpo, co-located with the 16th International Cloud Expo - to be held June 9-11, 2015, at the Javits Center in New York City, NY - announces that its Call for Papers is now open. The Internet of Things (IoT) is the biggest idea since the creation of the Worldwide Web more than 20 years ago.
As the Internet of Things unfolds, mobile and wearable devices are blurring the line between physical and digital, integrating ever more closely with our interests, our routines, our daily lives. Contextual computing and smart, sensor-equipped spaces bring the potential to walk through a world that recognizes us and responds accordingly. We become continuous transmitters and receivers of data. In his session at @ThingsExpo, Andrew Bolwell, Director of Innovation for HP's Printing and Personal Systems Group, discussed how key attributes of mobile technology – touch input, sensors, social, and ...
In their session at @ThingsExpo, Shyam Varan Nath, Principal Architect at GE, and Ibrahim Gokcen, who leads GE's advanced IoT analytics, focused on the Internet of Things / Industrial Internet and how to make it operational for business end-users. Learn about the challenges posed by machine and sensor data and how to marry it with enterprise data. They also discussed the tips and tricks to provide the Industrial Internet as an end-user consumable service using Big Data Analytics and Industrial Cloud.
Building low-cost wearable devices can enhance the quality of our lives. In his session at Internet of @ThingsExpo, Sai Yamanoor, Embedded Software Engineer at Altschool, provided an example of putting together a small keychain within a $50 budget that educates the user about the air quality in their surroundings. He also provided examples such as building a wearable device that provides transit or recreational information. He then reviewed the resources available to build wearable devices at home including open source hardware, the raw materials required and the options available to power s...
Things are being built upon cloud foundations to transform organizations. This CEO Power Panel at 15th Cloud Expo, moderated by Roger Strukhoff, Cloud Expo and @ThingsExpo conference chair, addressed the big issues involving these technologies and, more important, the results they will achieve. Rodney Rogers, chairman and CEO of Virtustream; Brendan O'Brien, co-founder of Aria Systems, Bart Copeland, president and CEO of ActiveState Software; Jim Cowie, chief scientist at Dyn; Dave Wagstaff, VP and chief architect at BSQUARE Corporation; Seth Proctor, CTO of NuoDB, Inc.; and Andris Gailitis, C...
There's Big Data, then there's really Big Data from the Internet of Things. IoT is evolving to include many data possibilities like new types of event, log and network data. The volumes are enormous, generating tens of billions of logs per day, which raise data challenges. Early IoT deployments are relying heavily on both the cloud and managed service providers to navigate these challenges. In her session at Big Data Expo®, Hannah Smalltree, Director at Treasure Data, discussed how IoT, Big Data and deployments are processing massive data volumes from wearables, utilities and other machines...
"There is a natural synchronization between the business models, the IoT is there to support ,” explained Brendan O'Brien, Co-founder and Chief Architect of Aria Systems, in this SYS-CON.tv interview at the 15th International Cloud Expo®, held Nov 4–6, 2014, at the Santa Clara Convention Center in Santa Clara, CA.
SYS-CON Media announced that Splunk, a provider of the leading software platform for real-time Operational Intelligence, has launched an ad campaign on Big Data Journal. Splunk software and cloud services enable organizations to search, monitor, analyze and visualize machine-generated big data coming from websites, applications, servers, networks, sensors and mobile devices. The ads focus on delivering ROI - how improved uptime delivered $6M in annual ROI, improving customer operations by mining large volumes of unstructured data, and how data tracking delivers uptime when it matters most.
In this Women in Technology Power Panel at 15th Cloud Expo, moderated by Anne Plese, Senior Consultant, Cloud Product Marketing at Verizon Enterprise, Esmeralda Swartz, CMO at MetraTech; Evelyn de Souza, Data Privacy and Compliance Strategy Leader at Cisco Systems; Seema Jethani, Director of Product Management at Basho Technologies; Victoria Livschitz, CEO of Qubell Inc.; Anne Hungate, Senior Director of Software Quality at DIRECTV, discussed what path they took to find their spot within the technology industry and how do they see opportunities for other women in their area of expertise.
While great strides have been made relative to the video aspects of remote collaboration, audio technology has basically stagnated. Typically all audio is mixed to a single monaural stream and emanates from a single point, such as a speakerphone or a speaker associated with a video monitor. This leads to confusion and lack of understanding among participants especially regarding who is actually speaking. Spatial teleconferencing introduces the concept of acoustic spatial separation between conference participants in three dimensional space. This has been shown to significantly improve comprehe...
The Industrial Internet revolution is now underway, enabled by connected machines and billions of devices that communicate and collaborate. The massive amounts of Big Data requiring real-time analysis is flooding legacy IT systems and giving way to cloud environments that can handle the unpredictable workloads. Yet many barriers remain until we can fully realize the opportunities and benefits from the convergence of machines and devices with Big Data and the cloud, including interoperability, data security and privacy.
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 15th Cloud Expo, Harold Hannon, Sr. Software Architect at SoftLayer, discussed how to take advantage of a multitude of compute options and platform features to make cloud the cornerstone of your online presence.