2010 NEM Summit 13-15 Oct. 2010 - Barcelona CATALONIA

As you may already know, the 2010 edition of the NEM Summit is set to be a not-to-be-missed event for all those interested in the Future Internet and in the fast paced evolution of the networked and electronic media industry.

Beyond a plenary session where senior industry leaders and high-level officials from the European Commission will present their views on the NEM research and industry landscapes, the event will feature four parallel tracks covering the technical, economical, and societal aspects of networked media and services:

• Green NEM
• Content Delivery
• User Centric Content Technologies   
• Innovative Media Services

A call for papers has just been launched
http://nem-summit.eu/call-for-papers
and we invite any interested stakeholders to submit scientific, technical or business-oriented papers.

The event will leave time for networking, providing in particular a key opportunity to meet partners and make final adjustments for proposals to be submitted at the first Future Internet PPP Call.

The event will also include a 2000 m2 exhibition area devoted to showcases and demonstrations of research results from key European and global players in the NEM area.
Cross-fertilization will be encouraged, namely in the framework of the first "electronic art contest" that the NEM European Technology Platform has just announced.
And much more for the 500+ awaited delegates!

Time has come to register, consider to exhibit, and submit a paper (submission deadline: July 21, 2010). EU projects are also offered the opportunity to answer a call for exhibition.

We would be pleased to answer any queries you may have or provide you with any further information you may need.
Looking forward to welcoming you in October in Barcelona.

Florent Genoux,
On behalf of the NEM Summit Organising Committee

The NEM Summit is organised by the NEM Initiative under the aegis of the European Commission with the support of the Spanish eNEM platform
Platinum Sponsors: BBC, Technicolor
Gold Sponsors: 3DLife, BCE, Tecnalia, Orange, Telecom Italia, STMicroelectronics, Intel, INRIA, IRT, NextMedia

Purpose
The purpose of the EPTS glossary of terms is to facilitate industry use of event processing technology by providing a common language for developing applications and software infrastructure that use event processing concepts.  The event processing glossary has three goals:

• Accelerate the learning of the event processing concept
• Further community communication by enabling practitioners to utilize common concepts and terms
• Provide a foundation for analysis and the development of best practices, publications, and industry standards

Organization
The glossary is presented according to the logical order of the terms. An index with an alphabetical listing is available at the beginning of the document for convenience.

Content
This glossary covers a small set of basic terms related to event processing. It will be frequently updated with additional terms in response to suggestions from the event processing community for improvements and additions.

Our approach is to define each term independently of any particular implementation, product, or domain of application. So, for example, the term event object has popular meanings as a tuple, a vector, a row, etc. These are all realizations of events in particular approaches and products. Even the most basic term, event, is problematic. 

Essentially there are two distinct meanings:

• An activity that happens
• Something that represents that activity in a computer system

It is tempting to introduce two separate terms such as event and event object. However, in any discussion longer than a paragraph or two, this becomes intolerably clumsy and one finds the distinction being misused, forgotten or dropped altogether. For example, using the two separate terms would dictate that -event processing (see below) should be -event object processing. The best solution is to overload the word -event. The context of each use becomes the indicator of which meaning is intended. This has been standard practice in the field of event-driven simulation for the past thirty years. It was the approach taken by the physicists of the early 20th century in discussions of relativity where -event also has two meanings. We have chosen to follow their example in the knowledge that it did not lead them into ambiguity problems.

Alphabetical Index of Glossary Terms

Abstraction
Architecture 
Architecture style 
Cause 
Clocks 
Complex event 
Complex event processing (CEP) 
Composite event 
Constraint (event pattern constraint) 
Derived event (synthesized event) 
Event 
Event (event object, event message, event tuple) 
Event attribute (event property) 
Event channel (event connection, event pathway, event topic) 
Event cloud 
Event-driven 
Event-driven architecture (EDA) 
Event pattern 
Event pattern triggered reactive rule 
Event processing 
Event processing agent (EPA) (event processing component, event mediator) 
Event processing language (EPL) 
Event processing network (EPN) 
Event sink (event consumer)
Event source (event emitter or event producer) 
Event stream
Event stream processing (ESP) 
Event template 
Event timing (timing) 
Event type (event class, event definition, or event schema) 
Granularity (chronon) 
Instantaneous event 
Pattern instance (event pattern instance) 
Raw event 
Relationships between events 
Rule (in event processing) 
Simple event 
Time interval 
Timestamp 
Virtual event 
Window

Glossary of Terms

Event

Anything that happens, or is contemplated as happening.

Examples:

• A financial trade
• An airplane lands
• A sensor outputs a reading
• A change of state in a database or a finite state machine 
• A key stroke
• A natural occurrence such as an earthquake
• A social or historical happening, e.g., the abolition of slavery, the battle of Waterloo, the Russian Revolution, and the Irish potato famine.

Event (event object, event message, event tuple)

An object that represents, encodes, or records an event, generally for the purpose of computer processing.

Examples:

• A purchase order (records a purchase activity)
• An email confirmation of an airline reservation
• Stock tick message that reports a stock trade
• A message that reports an RFID sensor reading
• A medical insurance claim document

Notes:

1. Events are processed by computer systems by processing their representations as event objects. The same activity may be represented by more than one event object; each event object might record different attributes of the activity. In many event processing systems, for example simulation systems, events are immutable. In such systems, a modification or transformation of an event must be achieved by creating a new event object and not by altering the original event. Deletion would entail removing an event from further processing.
2. Overloading: Event objects contain data. The word -event is overloaded so that it can be used as a synonym for event object. In discussing event processing, the word -event is used to denote both the everyday meaning (anything that happens) and the computer science meaning (an event object or message). The context of each use indicates which meaning is intended.

Virtual event

An event that does not happen in the physical world but appears to signify a real world event; an event that is imagined or modeled or simulated.

Examples:

• Instruction executions modeled by a hardware design simulation
• Events predicted by a weather simulation
• Events modeled by a war game
• Events that take place in a dream (“these dreams of you, so real and so true” ─ Van Morrison)
• Events in virtual reality

Event type (event class, event definition, or event schema)

A class of event objects.

Examples:

• The type of all price quotations
• The type of all sensor readings for any kind of sensor  

Notes:

1. All events must be instances of an event type. An event has the structure defined by its type. The structure is represented as a collection of event attributes (below).
2. Event types should be defined within the type definition system of a modern strongly typed computer language such as XML Schema or Java. Any standard for representing events will usually specify certain predefined data, examples of which might be:

• A unique event identifier used to reference the event
• The type of the event
• The time stamps of the event’s creation
• The source of creation for the event

Event attribute (event property)

A component of the structure of an event.

Notes:
An event attribute can have a simple or complex data type.  

Event processing

Computing that performs operations on events, including reading, creating, transforming and deleting events.

Notes:
The overloaded meaning event object processing is intended in this context.

Clocks

A process that creates an ordered ascending sequence of values of type Time with a uniform interval between them. Each value is produced at a tick (or clock tick).

Granularity (chronon)

The length of the interval between clock ticks.

Event timing (timing)

The time value attributes of an event.

Timestamp

A time value attribute of an event recording the reading of a clock in the system in which the event was created or observed.

Examples:

• Creation time: the time interval or time at which an event was created
• Arrival time: the time at which an event arrived at a point of observation

Notes:

1. An event can contain timestamps according to one or more clocks. For example, it can contain both its creation time according to a clock where it was created and its arrival time at a system location according to a clock at that location.
2. In systems with multiple clocks, the issue of clock synchronization is an ongoing topic of research. Not all timing attributes are timestamps. Timing in derived events, for example, may be derived from timing of the source events.

Time interval

A period of time bounded by two timing attributes called the interval’s start time and end time.

Instantaneous event

An event whose duration is less than the granularity of any clock that is applied in the system. An instantaneous event object will have a single timestamp signifying when the event happened. This is the special case where the time interval of the event has a measured length of zero (the start and end times are the same).

Notes:
An instantaneous event may have other timestamps with differing significances, e.g., arrival time. 

Cause

An event A is a cause of another event B, if A had to happen in order for B to happen.

Examples:

• The birth of a father and the birth of a son of the father;
• Sending an email and a reply to that email

Notes:
This is a definition of computational causality. It requires A to be necessary for B to happen. For example B’s father is a cause of B, but so is B’s mother. Other definitions of causality are possible, e.g., probable cause. The meaning and definitions of intentional or philosophical causality have been debated in countless books on philosophy.

Abstraction

An event is an abstraction of a set of events if it summarizes, represents, or denotes that set of events.

Complex event

An event that is an abstraction of other events called its members. 

Examples:

• The 1929 stock market crash – an abstraction denoting many thousands of member events, including individual stock trades)
• The 2004 Indonesian Tsunami – an abstraction of many natural events
• A CPU instruction -an abstraction of register transfer level (RTL) events
• A completed stock purchase -an abstraction of the events in a transaction to purchase the stock
• A successful on-line shopping cart checkout – an abstraction of shopping cart events on an on-line website

Notes:
A complex event denotes or signifies the set of its member events. It is tempting to say that a complex event references the set of its members, the implication being that the event contains a reference. In many cases this is true. But in general, reference may be too strong a requirement since it implies that the members can be determined from the reference. For example, there is no accepted agreement as to which events are members of the 1929 stock market crash.

Derived event (synthesized event)

An event that is generated as a result of applying a method or process to one or more other events.

Examples:

• An event reporting that company B has entered the bidding to take over A with probability 0.9, might be derived from an event reporting that the price of company A’s stock has jumped 10% in 5 minutes.
• The absence of an event, say in a given time interval, can lead to a derived event reporting that the first event did not happen.

Composite event

A derived, complex event that is created by combining base events using a specific set of event constructors such as disjunction, conjunction, sequence, etc. A composite event always includes the base (member) events from which it is derived.

Notes:
A derived event is not necessarily composite if its method of derivation lies outside a specified set of allowed constructors. The terminology composite and constructor are from the Active Database terminology.

Relationships between events

Events are related by time, causality, abstraction, and other relationships. Time and causality impose partial orderings upon events.

Notes:

1. Regarding the relationships of composite, derived and complex events: A composite event or a derived event is a complex event. The converses are not necessarily true.
2. The term aggregate event is sometimes used for some forms of composite or derived event.

Simple event

An event that is not an abstraction or composition of other events.

Raw event

An event object that records a real-world event.

Notes:
A raw event may represent a simple real-world event (e.g., the phone rang) or a complex real-world event. For example, the stock market crash of 1929 was a complex real world event that can be recorded by a complex raw event.

Complex event processing (CEP)

Computing that performs operations on complex events, including reading, creating, transforming, or abstracting them.

Notes:
CEP ultimately creates complex events even if some or all of the source events are simple events. See also the definitions for event stream processing (ESP), event streams, and event clouds, below.

Event source (event emitter or event producer)

An entity that sends events.

Examples:

• Software module
• Sensor
• Clock  

Event sink (event consumer)

An entity that receives events.

Examples:

• Software module
• Database
• Dashboard
• Person  

Event channel (event connection, event pathway, event topic)

A conduit in which events are transmitted from event sources (emitters) to event sinks (consumers).

Notes:

1. A channel can carry events of multiple types.
2. An event channel may be public (without access restrictions) or controlled.
3. An event channel is a medium for delivering one or more event streams.
4. A single event channel may be consumed by multiple event consumers. 

Event template

An event form or descriptor, some of whose parameters are variables. An event template matches single events by replacing the variables with values.

Examples:

• Send of any message
• String Msg; Send(John, Msg)

Event pattern

A template containing event templates, relational operators and variables. An event pattern can match sets of related events by replacing variables with values.

Examples:

• A pattern of events defining those sets of events in a completed sales transaction
• A pattern of events in an email correspondence: String Msg, Time T1, T2 ; Send(John, Msg, T1) and Receive(John, Msg, T2)
• A pattern defining the events in any successfully resolved customer complaint: Customer C, Agent A, Problem P, Time T1, T2, T3; Complain(C, P, T1) → Engage(A, C, T2) → Resolved (P, T3) 

Notes:
Event patterns can often be specified graphically.

Pattern Instance (event pattern instance)

A set of related events resulting from an event pattern where the variables are replaced by values.

Examples:

• end(John, See the NYT today, 15.00 EST) and Receive(John, See the NYT today, 12.05 PST).

Constraint (event pattern constraint)

A Boolean condition that must be satisfied by the events observed in a system.

Examples:
A service level agreement limiting the time taken to complete a mortgage transaction from the time an application is received.

Rule (in event processing)

A prescribed method for processing events.

Examples:

• Whenever three timeouts have happened, send an alert to the network manager.
• If more than ten shopping carts have been active for more than five minutes, then activate the website reaction time monitor and display an amber alert on the dashboard.
• Whenever IBM trades 2% above its 1 hour VWAP and then within 15 minutes trades 5 points below, then buy 1000 shares IBM.

Notes:
Event processing rules may be prescribed in many different ways, including by finite state machines, UML diagrams, graphical methods, Java code, SQL code, ECA (event-condition-action) rules or reactive rules that are triggered by event patterns (below).

Event pattern triggered reactive rule

A rule that prescribes actions to be taken whenever an instance of a given event pattern is detected.

Event processing agent (EPA) (event processing component, event mediator)

A software module that processes events.

Notes:

1. Event source and event sink are roles that an EPA may play;
2. One EPA could act in both roles – it could be an event source at one moment and an event sink at another time.

Event processing language (EPL)

A high level computer language for defining the behavior of event processing agents.

Event stream

A linearly ordered sequence of events.

Notes:

1. Usually, streams are ordered by time, e.g., arrival time;
2. An event stream may be bounded by a certain time interval or other criteria (content, space, source), or be open ended and unbounded.
3. A stream may contain events of many different types.  

Window

A bounded portion of an event stream.

Examples:
The events in the last ten minutes – i.e., a ten-minute moving window.

Notes:
Windows define subsequences of an event stream typically to focus the event processing on specific data or to improve event processing performance; however, they may also have other uses.

Event stream processing (ESP)

Computing on inputs that are event streams.

Examples:

• Applications that use stock market feeds as inputs and process events in their order of arrival to compute running average stock prices, volume weighted average prices over time windows, etc.

Notes:

1. ESP has its origins in active databases and data streams management;
2. The terms ESP and CEP are conceptual classifications. They can be useful in delineating philosophies of event processing and intended applications, but do not specify precisely the underlying capabilities of event processing engines.

Event cloud

A partially ordered set of events (poset), either bounded or unbounded, where the partial orderings are imposed by the causal, timing and other relationships between the events.

Notes:

1. Typically an event cloud is created by the events produced by one or more distributed systems.
2. An event cloud may contain many event types, event streams, and event channels.
3. The difference between a cloud and a stream is that there is no event relationship that totally orders the events in a cloud. A stream is a cloud, but the converse is not necessarily true.
4. CEP usually refers to event processing that assumes an event cloud as input, and therefore can make no assumptions about the arrival order of events. 

Event Processing Network (EPN)

A set of event processing agents (EPAs) and a set of event channels connecting them.

Notes:

1. The set of EPAs can be dynamic, i.e., EPAs can be created and destroyed. 
2. The set of channels can be dynamic, i.e., channels can be created and destroyed.
3. Dynamic behavior may controlled by patterns of events occurring in the network.
4. An EPN need not be an acyclic directed graph, e.g., feedback loops (cycles) are possible.
5. The runtime deployment of an event processing network may be distributed across multiple physical networks, computers, and software artifacts.
6. Various graphical representations of EPNs are possible. 

Event-Driven

The behavior of a device, software module or other entity whose execution is in response to the arrival of events from external or internal sources.

Examples:

• A cell phone
• An event triggered rule
• An operating system
• A bank’s trust department where the personnel spend their time putting out fires (i.e., event-driven rather than goal-driven or directed)

Architecture

(From IEEE) The fundamental organization of a system embodied in its components, their relationships to each other and to the environment, and the principles guiding its design and evolution.

Notes:

• Other definitions of architecture: The conceptual structure and overall logical organization of a computer or computer-based system from the point of view of its use or design; a particular realization of this.

Architecture style

(From Roy T. Fielding) A coordinated set of architectural constraints that restricts the roles/features of architectural elements and the allowed relationships among those elements within any architecture that conforms to that style.

Event-driven architecture (EDA)

An architectural style in which some of the components are event driven and communicate by means of events.

Notes:
This is a very encompassing definition of EDA in which systems that are only partially event driven are included. A purist would require all components to be event driven and all communication between them to be by events. However, some people hold that there are no real world business application systems in which all of the communication between the components is via event-driven relationships.

Glossary According to Lexicographic Order (definitions only)

Abstraction: An event is an abstraction of a set of events if it summarizes, represents, or denotes that set of events.

Architecture (from IEEE):
The fundamental organization of a system embodied in its components, their relationships to each other and to the environment, and the principles guiding its design and evolution.

Architecture style(from Roy T. Fielding): A coordinated set of architectural constraints that restricts the roles/features of architectural elements and the allowed relationships among those elements within any architecture that conforms to that style.

Cause: An event A is a cause of another event B if A had to happen in order for B to happen.  

Clocks: A process that creates an ordered ascending sequence of values of type Time with a uniform interval between them. Each value is produced at a tick (or clock tick).  

Complex event: An event that is an abstraction of other events called its members. 

Complex-event processing (CEP): Computing that performs operations on complex events, including reading, creating, transforming or abstracting them.  

Composite event: A derived, complex event that is created by combining base events using a specific set of event constructors such as disjunction, conjunction, sequence, etc. A composite event always includes the base (member) events from which it is derived. 

Constraint (event pattern constraint): A Boolean condition that must be satisfied by the events observed in a system.  

Derived event (synthesized event): An event that is generated as a result of applying a method or process to one or more other events.  

Event:  Anything that happens, or is contemplated as happening. 

Event (event object, event message, event tuple): An object that represents encodes or records an event, generally for the purpose of computer processing.  

Event attribute (event property): A component of the structure of an event.  

Event channel (event connection, event pathway, event topic): A conduit in which events are transmitted from event sources (emitters) to event sinks (consumers).  

Event cloud: A partially ordered set of events (poset), either bounded or unbounded. 

Event-driven: The behavior of a device, software module or other entity whose execution is in response to the arrival of events from external or internal sources.  

Event-driven architecture (EDA): An architectural style in which some of the components are event driven and communicate by means of events.  

Event sink (event consumer): An entity that receives events. 

Event source (event emitter or event producer): An entity that sends events.                                            

Event processing: Computing that performs operations on events, including reading, creating, transforming and deleting events    

Event stream: A linearly ordered sequence of events.  

Event stream processing (ESP): Computing on inputs that are event streams. 

Event pattern: A template containing event templates, relational operators and variables. An event pattern can match sets of related events by replacing variables with values. 

Event pattern triggered reactive rule: A rule that prescribes actions to be taken whenever an instance of a given event pattern is detected.  

Event processing agent (EPA) (event processing component, event mediator): A software module that processes events.  

Event processing language (EPL): A high level computer language for defining the behavior of event processing agents.  

Event processing network (EPN): A set of event processing agents (EPAs) and a set of event channels connecting them. 

Event template: An event form or descriptor some of whose parameters are variables. An event template matches single events by replacing the variables with values.                               

Event timing (timing): The time value attributes of an event.  

Event type (event class, event definition, or event schema): An event type is a class of event objects. 

Granularity (chronon): The length of the interval between clock ticks. 

Pattern instance (event pattern instance):A set of related events resulting from an event pattern by replacing the variables by values. 

Raw event: An event object that records a real-world event.  

Relationships between events:  Events are related by time, causality, abstraction and other relationships. Time and causality impose partial orderings upon events.  

Rule (in event processing): A prescribed method for processing events.  

Simple event: An event that is not an abstraction or composition of other events. 

Timestamp: A time value attribute of an event recording the reading of a clock in the system in which the event was created or observed.  

Virtual event: An event that does not happen in the physical world but appears to signify a real world event; an event that is imagined or modeled or simulated.  

Window: A bounded portion of an event stream.  

The virtual conference features event processing luminaries, early adopters and experts:

• W. Roy Schulte, Vice President and Distinguished Analyst, Gartner
• Opher Etzion, IBM Senior Technical Staff Member and chair of the Event Processing Technical Society
• Christopher Bird, Chief Architect at Sabre Airline Solutions
• Paul Vincent, CTO Business Rules and CEP, TIBCO Software
• Colin Clark, Chief Technology Officer, Cloud Event Processing, Inc.

Learn how Event Processing enables agencies and corporations to profit from continuous intelligence.

Hear from industry pioneers, leading vendors, and early adopters on Event Processing technologies and techniques that increase mission and business visibility and responsiveness.

Interact with industry experts, leading adopters, and peers via question and answer segments, and follow-on community discussion.

Influence, participate in, and benefit from the rise of event processing as we launch the Event Processing Community.

Featured Sessions

Smart Systems and Sense-and-Respond Behavior: The Time for Event Processing is Now
W. Roy Schulte, Vice President and Distinguished Analyst, Gartner
 
The need for situation awareness and sense-and-respond behavior is no longer limited to niche applications. Virtually every large new system in business, defense, and government is becoming event-driven in some aspects of its operation. This overview session will introduce the fundamentals of event processing, and explain where it is used and why it is a fundamental part of smart devices and smart applications of all kinds.

• How does continuous monitoring enable situation awareness and provide earlier detection of threats and opportunities?
• What design patterns form the basis of smart devices and event-driven processes?
• Which technologies and product categories are applied to different event processing usage scenarios?

Analyze, Sense, and Respond: Identifying Threats & Opportunities in Social Networks
Colin Clark, Chief Technology Officer, Cloud Event Processing, Inc.

Colin Clark will demonstrate the use of streaming map/reduce and complex event processing to identify threats and opportunities using Twitter. This session will identify specific technologies, how they're inter-related, and how the DarkStar and Telescope products from Cloud Event Processing can be used to do this quickly and efficiently utilizing elastic resources.

• What are short lived trends in Twitter and how can they be taken advantage of?
•  What are people saying about your company on Twitter and is it positive or negative?
• How do Twitter usage patterns differ geographically and what opportunities does this present?

Case Study: Event Distribution Architecture at Sabre Airline Solutions
Christopher Bird, Chief Architect at Sabre Airline Solutions

Christopher Bird will present the event distribution architecture in use within Sabre Airline Solutions. This event distribution architecture is used as the backbone for delivering events and content through the high volume, reliably network within Sabre.

Of special interest is overcoming the following issues:

• Large payload delivery
• Validation in the delivery network
• Situational awareness
• Management of control structures
• Message redelivery and failure semantics
• Use of the event network for test data distribution

Event Processing - Seven Years from Now
Opher Etzion, IBM Senior Technical Staff Member and chair of the Event Processing Technical Society

While event processing is considered as an emerging technology in enterprise computing, it has barely scratched the surface of its potential. This talk will illustrate a world in which event processing is everywhere, consumed by everybody, and used for enterprises as well as consumers.

First, we survey six trends:

• Going from narrow to wide coverage
• Going from monolithic to diversified architectures
• Going from propriety to standards in various areas
• Going from programmer-dependent application development to user independence in application development
• Going from event processing as stand-alone technology to event processing as pervasively embeddable technology
• Going from using event processing reactively to using event processing also proactively

For each of these trends a roadmap will be described as well as the target goals. In addition there will be a discussion on four areas which require development beyond the current state of the art in order to realize the "event processing anywhere" vision: event processing virtualization, event processing software engineering, event processing scalability and intelligent event processing, in each of these topics we discuss the challenges and directions, and the way they play in the bigger picture.

Events, Rules and Processes - Exploiting CEP for "the 2-second Advantage"
Paul Vincent, Chief Technology Officer, Business Rules and CEP, TIBCO Software

Complex Event Processing technologies are normally associated with higher performance investment banking decisions - providing a trader with an edge over the competition. However, the advantages of the simplicity and performance of the "event processing" ideal has also attracted the attention of other businesses requiring processes and services with very high throughput yet handling multiple scenarios and policies.

Many of the ideas present in the BPM and SOA state of the art are also present in CEP systems: event bus, rules engine for decisions, model-driven development and such. Additionally, CEP adds a focus on "event", "time", and their associated pattern-based processes - which possibly better reflects the cognitive processes we undertake ourselves.

In this session, we explore some of the design patterns and case studies that exploit CEP technologies to provide adaptive event-driven processes and services. As well, we discuss why the architects deviated from the safe road of "the orchestration diagram and BPEL" to the more rare declarative rules and the other models associated with event pattern detection - for SOME of their processes and services!

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GOSPEL : General Olfaction and Sensing Projects on a European Level

Artificial olfaction
offers the most promising route to overcome the current limitations of
gas sensing technologies, but as an inherently multi-disciplinary field
suffers from fragmentation and lack of communication. The network of
excellence GOSPEL (General Olfaction and Sensing Projects on a European
Level) aims to establish Europe as a world leader in science,
technology development and subsequent commercial exploitation of
artificial olfaction and gas sensing.



GOSPEL will provide a landscape of European capability in
artificial olfaction and will identify the barriers to its exploitation
and provide measures to overcome them. It will identify services
required by industry, develop the offering and put in place the
necessary legal and organisational structures to implement them. The
network aims to produce a long-term restructuring of European research
through the formation of a revenue-generating self-sustaining entity, a
European Research Interest Group (ERIG) based on the legal model of an
EEIG. GOSPEL will enable early-stage and established researchers to
develop multi-disciplinary approaches to this subject through a
comprehensive training and exchange programme.



The scientific goals include:

(1) thorough understanding of the biological sense of smell and
mimic its relevant functions,

(2) provision of an objective, quantitative assessment of gases
and odours, and

(3) provision of the means to monitor technically relevant and
non-odorant gases even in complex mixtures.



The participants¿ expertise crystallizes around three focal
points, the centres of excellence (CoE), dedicated to: microsystem
research ("hardware"); the development of algorithms and data
processing methods ("software"); fundamental research in biological
olfaction and data processing ("biomimetics"). The technological centre
of gravity will be the development of highly integrated microsensors in
silicon technology to enable more robust and stable gas sensing
products with wide applicability.

Participants

<!--

Coordinator: EBERHARD KARLS UNIVERSITAET TUEBINGEN, GERMANY -->

Coordinator: EBERHARD KARLS UNIVERSITAET TUEBINGEN, GERMANY

Ubiquitous Complex Event Processing (U-CEP) Rainer von Ammon
Email
rainer.ammon@citt-online.com
Institution/Company
Centrum für Informations-Technologie-Transfer GmbH
Position
Managing Director
Country
DE
Permission
yes
BRIEF EXPLANATION
Already since a long time when we try to explain the phenomena of the world, philosophers and researchers are referring to a universe based on events (see e.g. the research about synchronicity of events in modern physics or psychology). The nature of ‘events’ is not the simple concept of current generations of computing systems. It needs new understandings, methods of handling, and most of all computational resources. An emerging discipline of Complex Event Processing (CEP) introduced the concept of events in the computer science as a new paradigm [1, 2, 3, 4, 5, 6]. CEP underpins all computation that addresses complexity phenomena. As examples:
• Sequences of happenings across time and space require to be captured to model global phenomena in the natural world.
• The world ecology, the bio-model and mankind expressed through Society evolves over time and space.
• Most of all intentional behavior consists of sequences of responses over time and space.
The above statements presume first some understanding and definition of what topology of time-space seems to be relevant to a particular domain. Even in the physical world this is not simple. In the biological world the evolution of the species is not the whole story. Human behavior and social patterns have been discoursed upon since Plato (or earlier, his mentor Socrates).
In today’s world, Complexity makes all these aspects very difficult to handle; this is the world of dynamics, interactions, and all the interesting outcomes from emergence including sudden changes in state that forewarn of a need for ‘special action’. This is the world of Complex Adaptive Systems. Three examples show why the ability to compute ’complex adaptive systems’ is important:
• FuturIcT [C1] has identified critical applications such as: understanding and predicting societal phenonema and behavior including risk management, counter terrorism, fraud management, epidemic, global emergencies etc.
• INTEGRAL BIOMATHICS [C2] examines the fundamental computational basis of bio- and socio- systems. It anticipates the need for a new breakthrough paradigm change towards biologically and socially driven mathematics and computation and the technological challenges of bringing this about. Its goal will be a set of novel mathematical formalisms.
• SOCIONOME-METALOGER[C3] will establish the new science of Computational Socio-geonomics based on the coupling of science and social systems within the new ICT paradigm.
• World Ecology Modeller (S-GAIA) [C4]]has identified that Society is a complex adaptive system and processing its events, the product of intentionality, lies at the centre of improving the outcomes of action.
• Computational event processing applies on vast scales to disciplines as different as Bio-sciences, fundamental energy/matter, the brain and physiome. The list is endless.
Prof. David Luckham/Stanford University as one of the main inventors said about the forecast of CEP that within the next decades the paradigm of Ubiquitous Complex Event Processing is predicted as the precondition of an execution and simulation platform for a lot of more global and interrelated applications. Current computing methods to handle complex phenomena are not well understood or developed and the ability to handle the scale of event traffic does not exist yet. An IBM sponsored report said:
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“ .... the transparency demanded by regulation (such as the Markets in Financial Instruments Directive (MiFID)) requires visibility into the operation of business networks, which again is dependent on electronic information flows. Furthermore, the stresses and strains these forces exert on organisations are changing all the time, and this means that organisations have to be able to sense and respond to internal and external changes... “
This is Global Dynamics, Policy management, dynamics of complex interactions, understanding and management of the world ecology . This is the context of Ubiquitous Complex Event Processing.
AMBITION
Vision
Ubiquitous COMPLEX EVENT PROCESSING is an Industry led initiative to exploit the opportunities of the science of CEP into the mainstream of computational practice across diverse fields of science, not just the aspect of computational modelling but the fundamental physical world phenomena exhibited. Elucidation of this discovery will lead to new applications of CEP across the bio-ecological world, and all the artefacts of human involvement and management of this ecology, such as: commerce, industry, government and Society. It will be the ubiquitous applied technology for processing the dynamics of complexity in real world systems. It involves: fundamental science; elucidation of the computational meaning of ‘event‘; development of technologies appropriate to the scale of domains of different event patterns; their incorporation into modelling platforms that are useful to their clients – scientific fields, society, the world ecology.
Science of CEP
The fundamental hypothesis of CEP is that the pattern of events determines the existence, identity, significance and outcomes of every living entity within the biosphere. The complex interactions this hypothesis involves need to be understood observed and the means found to influence them. This may sound like interfering with our world ecology and very existence. In fact the reverse is the case: currently damage to our biosphere is the result of ignorance; reversing this is a moral quest.
The message of many disparate studies is that complex events in the real world are the triggers of action, or even more profoundly, of identity; identifying, processing, and responding to them is the canvas of CEP. Examples show this ubiquity and the enormous variation in scale, that work in combination:
• Chemistry and bio-chemistry are about CEP, studies by scientists such as Professor Peter Plath.
• The discipline of the New Biology of Epigentics studies the environment as a global cloud of signals, energies or so called events are at the end responsible for the way of life of a cell.
• At the level of basic energy and matter quantum physics and the “Heisenberg uncertainty relation” defines events as eddy of quarks and photons that are a high-end challenge for the CEP-technology.
• The human physiome is a united cell structure of 50 trillions of single cells where each cell is doing event processing on the base of its receptors of its cell membrane. So, a human being can allegedly process 120.000 events per second unconsciously (by the right side of the brain), but logically a human can only process a few information units at the same time (by the left side of the brain).
CEP researches and mimics the bio-logic, structures, and methods because it is a computational model of the same phenomena. Event Processing Agents (EPA’s) are actually cells where event adaptors are the receptors of a cell membrane and the event processing logic based on an Event Processing Language (EPL) are the effectors of a cell. Event Processing Networks (EPN’s) are actually a united multicellular structure and so on. The Epigenetics explains how the environmental signals (events) control the activity of the genes. The primacy of the DNA is no longer valid, and the new found information flow is now called the “primacy of the environment”. Recent experiments of The Epigeneitcs have proved that our beliefs and thinking is energy in the sense of environmental signals. The Epigenetics found that all these kinds of environmental signals influence the regulating proteins which control the activity of the genes and that the global event cloud as environmental signals influences and changes the DNS (so called reverse transcriptase). In the realm of society in addition to the bio-cellular model, every construct of social system is a newly designed constellation of interacting cells defined to achieve a social/business/organization purpose. The details are the properties defined in the SOCIONOME of Society.
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The Technology of CEP
The first requirement is to be able to construct a model of the topology and features of the global event cloud; in most cases this is itself evolving and always specific to the scientific field of study. However fundamental features can be abstracted, e.g. in the case of complex adaptive systems, and top-level normative models used as templates for designing systems to process that event cloud.
As with most systems oriented work, there is a progression to standardized modeling techniques, a language of discourse that can be applied to construct, ‘compile’ processable environments to conduct experiments on the observable phenomena. The entire process is iterative and evolutionary. The learning that takes place is to find meaningful patterns in the dynamics of sets of experiment and action. This always involves the relation between experiment and experimenter.
This project will research and devise technological solutions appropriate to the different kinds of entity discussed above, the scale of the cellular interactions that belong to the life of that entity, the specific events and the required response of the entity. In the business/organization world this is the total set of events arising from the conduct of the business/organization at every level from everyday activity to the highest levels of governance within the world ecology. It will position this research in the context of extreme complexity and the tools required to deal with this in the life-cycle of each ‘species’ of bio-socio life. This can be envisaged as the potential interactions of the entire set of competing and cooperating species and their members, living out their purpose and consuming the resources of the ecology in doing so. This requires processing the dynamics of multi-layered complex events. It requires the relation of multiple ontologies to be constructed and modeled. The technology has to integrate on a scale required to deal with this complexity, involving identification and responses to events in huge numbers of concurrency. The technology involves continuous re-configuring since all facets of the processing are themselves complex adaptive systems.
IMPACT
The fundamental importance of U-CEP is that it reveals the dynamics of the environment of a bio- or-socio- entity which condition that entity to behave in particular ways and to construct and execute strategies to deal with that environment. The question U-CEP answers is how to achieve sufficient mastery of the environment and the events that permeate it, the ‘global event cloud‘, to influence the otherwise chaotic outcomes, or the outcomes determined by alien peturbations.
Ultimately such experimentation across the universe of observable phenomena will lead to new and novel understandings of how anything and everything becomes instantiated, lives out a life, and gives way to some other phenomenological pattern. The most interesting question is that of quantum experimentation: who decides what is really happening?
This would seem to be of immediate import in the social sciences field where the questions of goals, influence, behaviours and outcomes for the world ecology are determined by the models that are designed, but still influenced by clouds of events not obviously triggered by designed models. Resolving such conundrums is of crucial importance.
The outcome of this work will be progressive compilation of ordered views of the model topology and complex event changes to instantiations of this, which are then translated into inferences about what is happening, its significance, and even ultimately its ‘meaning‘. The latter takes U-CEP into the important realms of human intelligence and consciousness, the paradigms of meaning already constructed, and paradigmatic shifts to new models.
INTEGRATION
U-CEP requires the twin worlds of domain behaviour and computational advances to be combined in order to design and execute experiments and operational environments that process complex events. The reality of CAS is there is no deterministic solution in advance or ever. As the IBM Paper said:
“ Although event processing technology is not trivial to understand, you need to find ways to get IT specialists working with business domain experts.... Domain experts have the most useful insights into significant events and patterns, decisioning rules and actions – the “content” for event processing systems – ‘‘U-CEP research concerns fundamental questions that are both universal and specific to each area, typefied by the following example areas that is a conceptual canvas of the required research:
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1. David Luckham’s forecast 2008 – from Simple CEP to Ubiquitous CEP in an event-driven world
1.1. Which are the applications of Ubiquitous CEP of the next decades? Neuroscience, Epigenetics, Brain Research, CERN Large Hadron Collider, Higgs Boson, …
2. Ray Kurzweil’s understanding of CEP – “Singularity is Near”
2.1. Mapping of Ray’s and David’s forecast until 2050 and beyond
3. Enhancing human intelligence and cognitive or physical abilities
3.1. Jeff Hawkins’s understanding of CEP – “On Intelligence”
3.2. Memory-prediction framework
3.3. Prediction and (event) patterns
3.4. Intelligent machines – based on a layered CEP like human brain’s cortex
3.5. The Blue Brain project – Henry Markram (EPFL Lausanne)
3.6. Brain Computer Interface – Kevin Warwick (Reading University)
4. Epigenetics and CEP – Bruce Lipton’s “Biology of Belief”
4.1. Cell membrane as an organic information processor, receptors, effectors, protein machinery, reverse transcriptase and event processing, an individual as a combination of fifty trillion of collaborating event processing cells
4.2. Event adapters, Event Processing Agents, Event Processing Networks as the analogy of CEP
4.3. Epigenetics-research-portal for Germany, Austria, Switzerland
4.4. Rationale of EU FET-program: Biological cells are highly sophisticated, chemical information processing systems, capable of responding to changing conditions. The information processing capabilities of such systems could be exploited by future information technologies if this ‘information chemistry’ could be ‘programmed’.
4.5. General objective of EU FET-program: Develop the foundations for a radically new kind of information processing technology inspired by chemical processes in living systems. Enable the development of ICT systems and devices that utilize interactions between components to assemble complex functional information processing materials. The research should enable a new generation of systems capable of interfacing with conventional IT systems that are self-replicating, selfrepairing and/or capable of rapid adaptation/evolution as well as flexible reconfiguration in response to changing conditions
5. Brain research and CEP – the long and the most recent discussion about “Free Will”
5.1. The position of the brain researchers Wolf Singer, Gerhard Roth et al.
5.2. The world as a deterministic whole based on Event Processing
5.3. Concerns, e.g. of Jürgen Habermas
5.4. The Blue Brain project – Henry Markram (EPFL Lausanne)
5.5. Brain Computer Interface – Kevin Warwick (Reading University)
6. Consciousness and memory technology – Storing, modifying, transferring of consciousness and the relation with CEP
6.1. Theory of Hans Moravec, Otto E. Rössler, Ray Kurzweil
6.2. Concerns, e.g. of Klaus Heinerth, University of Munich
6.3. Individual consciousness, consciousness of groups, cosmic consciousness (Jörg Starkmuth) – what would we store, modify, transfer?
7. Universe and Events – Large Hadron Collider of CERN
7.1. Higgs Boson, Higgs Field, Big Bang, From energy to matter
7.2. Event processing at CERN means:
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- 600 millions events per second, a lot of sensors for different event types
- event filtering, event enrichment, event processing as cloud computing by more than 100.000 computers, at present C++ coded
- goal: find the Higgs boson
Questions to be discussed could be:
- strategy for the detection of Higgs boson
- can be solved by a CEP approach?
- what kind of EPL would be appropriate?
- is there a need for a flexible and fast changing EP-logic?
Etc.
8. Computational Socio-Geonomics
8.1. Concept of Weak and Strong Emergences à to be mapped on Complex Events
8.2. An ICT platform for Social Simulation means:
- around 10 billion human agents in future
- millions or even billions and trillions of events per second,
- a lot of sensors for different event types (“smart dust”, foglets)
- appropriate modeling approach for simulation scenarios
- easily to use and domain-appropriate Event Programming Language
9. A list of use cases and scenarios from the Zurich workshop “European FET Flagship Initiative”:
- interdisciplinary and systems thinking to advise private and public organizations on such matters as diverse global trends, plausible scenarios, emerging market opportunities, and risk management
10. Discussion about future interdisciplinary CEP-based projects:
10.1. SmartHealthcare and Diabetes: Simple CEP/ed(B)PM?
10.2. SmartHealthcare and Depression: Depression - A defect of Event Processing?
10.3. SmartEmergencyManagement: City/Location specific emergencies as Simple CEP/ed(B)PM versus global emergencies taking the example of Solar Storm 2012
10.4. Brain Research: A new Libet experiment regarding Free Will based on CEP?
The application of U-CEP thinking has been accepted as a relevant discipline in the FuturIcT project Virtual World Modeller (VWM) where it will lead the focus on complex events and the technologies to handle them.
Thus this project will provide insight into the fundamental questions to be asked, and hopefully reach some conclusions leading to processable constructs, that term applying to both the modeling framework and its computational representation.
PLAUSABILITY
As stated above, computing has always addressed ‘events’. The new work envisaged in this project is to raise the level of this to the ability to deal with the phenomena of complex events, occurring in vast numbers and scales.
Progress Beyond the Current State of the Art
The drivers for this research are the growing recognition across every field of science that their phenomena are rooted in the basic questions regarding what determines their existence and how is this manifest in observable, measurable, and predictable collections of entities and events.
The above definition is a blueprint for the construction of a model, also termed a hypothesis, that relates to existing Frames of reference in our world, and that can be translated into a computational form. Since it
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is now becoming clear that complexity is the nature of all phenomena even where limited abstractions are amenable to deterministic rules, a convenience to support bounded reality, there is no alternative game in town. The pursuit of modeling in both domains and computers is ubiquitously directed to addressing indeterministic systems.
Road Map
The outcome of this effort is leading both real-world experimentation and computational representation to new fundamental methods and solutions. The experimental means are the focus ahead of its application to derive insights, answers and real world action.
The infrastructure for this is a combination of pure science, applied science, technology and engineering, and the application of all these fields of research in real world applications. The practical signs of progress and achievement are the formation of institutions to oversee the progress, consolidate the research, and command the resources and funding to deliver results. The following are specific examples:
• The Complex Systems Society is spearheading the drive to bring the science into the mainstream of practical application, especially science and computational methods
• The Event Processing Technical Society has been formed to spearhead and consolidate the many disparate efforts to deliver practical computational approaches by industry. It is leading the required drive for standards and methods that will avoid the Tower of Babel syndrome
• Events, Conferences, practical applications and pure science are underway in many fields.
Aspirational dates, forecasts until 2020 and Beyond, e.g.:
• See the forecast of David Luckham [1]
• See the forecast of Ray Kurzweil [7]
All these are the real signs of a new branch of science moving from theory to practical use.
Implementation
Benefits
This project will bring the science and technologies of Ubiquitous Complex Event processing into the mainstream of ICT and all the real-world applications dealing with complexity. Conclusions
U-CEP is a critical new field of pure and applied science and engineering that will deliver ICT capability that can support the solving of practical problems involving critical time and space based dynamics (where these dimensions also include further levels of abstract ontological entities). It is thus a critical component of many fields of enquiry involving real-world complex adaptive systems
References
1. David Luckham: The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems. Addison-Wesley 2002
2. Opher Etzion, Peter Niblett: Event Processing in Action. Manning 2010, http://www.citt-online.com/downloads/EPIA%20for%20experts%20meeting.ppt
3. K. Chandy, W. Schulte: Event Processing: Designing IT Systems for Agile Companies. McGraw-Hill 2009
4. von Ammon, R. 2009 Event Driven Business Process Management. In Encyclopedia of Database Systems, Ling Liu and M. Tamer Özsu (Eds.), Springer
5. von Ammon, R., Ertlmaier, Th., Etzion, O., Kofman, A. and Paulus, Th. 2009 Integrating Complex Events for Collaborating and Dynamically Changing Business Processes, ICSOC/ServiceWave 2009, Mona+ workshop, Nov 23-24, 2009 Stockholm, Sweden, DOI= http://www.citt-online.com/downloads/Integrating_Complex_Events_for_Coll...
6. von Ammon, R., Ertlmaier, Th., Etzion, O. and Paulus, Th. 2009 Existing and future standards for event-driven business process management, Proceedings of the Third ACM International Conference on
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Distributed Event-Based Systems DEBS 2009, July Nashville, USA, DOI http://portal.acm.org/citation.cfm?id=1619258.1619290
7. Ray Kurzweil: The Singularity Is Near: When Humans Transcend Biology. Viking Adult 2005
8. Jeff Hawkins: On Intelligence. Times Books 2004
9. Blue Brain Project http://en.wikipedia.org/wiki/Blue_Brain_Project Henry Markram: http://www.youtube.com/watch?v=LS3wMC2BpxU
10. Bruce H. Lipton: The Biology of Belief. Mountain of Love 2005
11. Dennis Bray: Wetware: A Computer in Every Living Cell. Yale University Press 2009
12. Wolf Singer: Der Beobachter im Gehirn: Essays zur Hirnforschung. Suhrkamp 2009
13. John Morgan Allman: Evolving Brains. W.H. Freeman & Company 1999
14. Hans Moravec: When will computer hardware match the human brain? Robotics Institute, Carnegie Mellon University, Pittsburgh 1997, http://www.transhumanist.com/volume1/moravec.htm
15. Hans Moravec: Die Wirklichkeit ist ein Konstrukt des Bewußtseins. http://www.heise.de/tp/r4/artikel/6/6038/1.html
16. Jörg Starkmuth: Die Entstehung der Realität. Bonn 2008
17. Klaus Heinerth: Können Computer Bewußtsein entwickeln? 1998, http://www.heinerth.de/Computerbew.htm
18. Rolf Landua: Am Rand der Dimensionen: Gespräche über die Physik am CERN. Suhrkamp 2009
19. R. Sun, Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation. Cambridge University Press, New York. 2006.
COMMENTS
The following is a list of typical active partners; it is only a sample:
• The World Society Modeller (S-GAIA) Consortium
• The Event Processing Technical Society (EP-TS)
The most of the FET proposals are based on some features of the Future Internet (FI).
• European Future Internet Initiave (EFII)
• Networked European Software & Services Initiave (NESSI).
Although we will and have to cooperate with the organisations around FI (e.g. providing an event streaming interface to FI), the research topics of FI and U-CEP will be carefully separated.

12:00 12:15 Introductions, main ideas and available material

12:15 12:30 SoundITs SHORT STREP

12:30 12:45 FETOPEN Flagship meeting  9-10June2010

12:45 13:00 Round of questions

1) template of LoI of the FETOPEN
2) two stages the first SHORT STREP is anonymous and the second is a traditional EPSS proposal
Now we have to work on this SHORT STREP to be sent in firsts of June
Look at the draft of the proposal sent was is needed for the SHORT STREP
The final STREP has to be a 3ME 3years 6-8 partners STREP
  We have to manage the first feedbacks received to asks some points of the ShortStrep evaluation and made an appointment to discuss the preparation of the STREP. This is usually a short meeting during the workshop.
 
Maybe in this FET-OPEN we will consider SensingITs to deal multi-sensing models for robotics systems, in particular Acoustic and Odour Events Detection (AED) and (OED) in parallel. We have to work in the fitting of IDMT and  IAST competences and works
http://aass.oru.se/~ali/icra10ws/index.html

Short proposal  ICT FET Open Call FP7-ICT-2009-C
Work programme topics addressed  ICT-2007.9.0 FET Open
Type of funding scheme: STREP Small or medium-scale focused research project (SHORT PROPOSAL)

Information technologies and models for endowing auditory capacities and embodying human audition faculties
into robotic systems

SOUNDITS

Date of preparation: 11Jun10 (28Sep10)