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SMART VORTEX is a Large Integrated Project co-financed by the European Union within the Seventh Framework Programme.

The goal of SMART VORTEX is to provide a technological infrastructure consisting of a comprehensive suite of interoperable tools, services, and methods for intelligent management and analysis of massive data streams to achieve better collaboration and decision making in large-scale collaborative projects concerning industrial innovation engineering.

One of the core objectives of the so-called SMART VORTEX Suite is to leverage knowledge hidden within the massive data streams throughout the lifecycle of the project. To achieve this, the tractable data has to be captured, then the pertinent aspects of information have to be identified, inferred, and delivered to the right place in an efficient way . The second core objective of the project is to use this information effectively for supporting collaboration and decision making . In addition, collaboration and decision making processes themselves are rich sources of critical tractable information, which has to be treated accordingly. As illustrated in Figure 2, these aspects are tightly intertwined and generate a cycle of activities revolving around the SMART VORTEX of product data streams.

 

All activities revolve around the SMART VORTEX of product data streams.

In SMART VORTEX, capturing tractable data means to define models for describing data sources, streams, and sensor interfaces and use these schemes and sensors to produce product data streams from sources in all phases of the product lifecycle. This results in very large raw bit streams. As illustrated in Figure 3, sensors and processing enrich data streams with semantic RDF-based annotations for tracking provenance, evaluating trust level, authenticity and assessing reliability, resulting in more expressive data streams. The SMART VORTEX of data streams is handled by a Data Stream Management System (DSMS) which provides scalable and efficient search and processing of all information, also based on these annotations. It will be possible to develop plug-ins containing new functions for computations, filters, services, triggers, feature extractions, and any kind of other analyses, to be used in queries as higher level operators increasing their overall expressiveness. The sensors are parts of products in use or services which are inferring new higher level events. As illustrated in Figure 3, this results in more refined data streams. This scheme of annotations and stream inference can be extended recursively, resulting in more and more semantic data streams, as compared to the initial raw bit streams. Additional sources for streams are dedicated sensors which are integrated in collaboration tools capturing design, social interaction, user behaviour, and decision making processes, enabling improved tracking of provenance and improving the tools for design and decision making. Besides various on-line streaming data sources, regular databases, data stored in PDM systems, and archived data streams, such as log files, will be used.

 

Enriching individual product data streams in the SMART VORTEX by annotation and inference.

 

Delivering pertinent information means to deliver the right information at the right time from within the SMART VORTEX in a way which is consumable by humans or higher level software services. To achieve this, it is necessary to improve the information lifecycle within the product lifecycle. Based on the captured tractable information, i.e., the SMART VORTEX of semantically enriched product data streams, different methods for large-scale processing are deployed within the DSMS. These methods depend on the individual application domains, as for example the processing of sensor data from motors differs significantly from processing signal integrity simulations in electronic design. These methods are based on scalable streaming approaches for search and analysis of sensor data and services for collaboration analysis, and feature extraction emitting streams of higher level inferred events. Such events range from exceptional conditions within the operation of products at customer sites to knowledge about design rationale and analysis of collaboration regarding parameters, such as, commitment, quality, impact, shared understanding, progress and quality of intermittent results.

In addition to the automated information processing and reasoning on the product data streams, the data has to be made accessible to humans in personal and collaborative environments. This is done by visual tools that provide innovative user interfaces to perform querying over the smart vortex and display the results in a graphical way, thus allowing navigation and, hence, information consumption. Query results are visualized as a visual display and sequences of visual displays. To provide humanly manageable access to the smart vortex, a Visual Query Language specifically hides the complexity of the formal language for data stream queries behind an extensive use of the metaphor of graphical objects (e.g., icons, diagrams, forms, tables). A Multimodal Query Language extends the capabilities of the visual interfaces to provide a more natural interaction, exploiting e.g. the new interaction concepts, especially multitouch and voice, which are now adopted in the mainstream through recent operation system releases.

Sustainable collaboration and decision making processes are critical skills and competences in organizations. To support these critical skills in projects, SMART VORTEX provides a social collaboration platform to enable collaboration support in a way that supports the capturing of different data streams including social contexts, team competencies, social interaction, collaboration, and other relations valuable for product development companies in cross-organizational scenarios. The social collaboration platform supports the adoption of vocabularies and rules for professional product development work and product data stream annotation. The platform provides a runtime environment and means of integration for individual collaboration tools and services, granting rule and policy-based access to the SMART VORTEX. In addition, it integrates domain specific engineering tools through a service-oriented architecture. The individual tools and services will use the SMART VORTEX of product data streams as a core communication metaphor, enabling a more integrated, partly real time, interaction between users, engineering tools, and massive data streams and options for annotating these streams with collaboration based and social metadata.

The DSMS query tools allow end-users to perform mining and aggregation over the smart vortex of data streams so as to discover the current trends of streams and, thus, infer hidden information. The DSMS and its tools for data stream mining and querying are of extreme importance for the multi-level decision support service system. The inference mechanisms include methods for identifying decision making processes. Such events are important parts of documenting the provenance of product data, and are used to annotate other data streams, such as product design data. In addition, services use intelligent strategies for automated intervention to advice engineers and project leaders in collaborative work practices. Furthermore, the collaboration tools will integrate support for DSMS-based product data processing, giving the collaborating partners immediate access to the relevant data to base decisions on.

The inference mechanisms include methods for identifying decision making processes. Such events are important parts of documenting the provenance of product data, and are used to annotate other data streams, such as product design data. In addition, services use intelligent strategies for automated intervention to advice engineers and project leaders in collaborative work practices based on inferred product data streams. Furthermore, the collaboration tools will integrate support for DSMS-based product data processing from all phases of the product lifecycle, giving the collaborating partners immediate access to the relevant data to base decisions on.

To provide scalability of this additional workload created by the computations within the DSMS, SMART VORTEX uses approaches from parallel computing and distributed architectures which push computation down as far as to the sensor network level. This means, that some of the computations are done in the devices in use before being transmitted via a network connection, to the core infrastructures which in turn are based on a parallel processing architecture for scalability.

By nature, data streams from sensor networks, simulation, and collaboration are to a certain degree uncertain, incomplete, and inconsistent. In SMART VORTEX, this is a result of the circumstances found within the individual application domains. The industry partners are already used to handle data with these constraints. Thus, the resulting problems are solved on the application level at industry partners and are in parts fed into the research for algorithms for analysis, filtering, and annotation.

Within large-scale design and engineering projects, different industrial partners collaborate, while the same partners are competitors in other projects. While sharing information for collaboration is necessary to be successful in the project, it is also mandatory to have a fine grained control over the information which is shared, i.e., black box collaboration, with the otherwise competing partner in order to protect the individual intellectual property. SMART VORTEX defines a rules and policy framework that enables control over cross-organizational IPR management, allowing efficient black box collaboration.

In SMART VORTEX, design and engineering is viewed from the perspective of functional products. A functional product is a system consisting of hardware that together with a support system delivering the agreed upon function. The support system consists of software and various services. It keeps the whole system, including hardware, operational and able to deliver not merely hardware functionality, but available productivity for the customer. For example, a functional product with a hydraulic motor as its hardware core would output available torque per hour as its function.

By contrast, in a traditional sale of hardware, the supplier sells hardware (for example a motor) and gives a certain warranty. When the warranty period is over the supplier earns money on repairs and spare parts. In the sale of a functional product the supplier keeps ownership of the hardware and the support system and sells the total system output, i.e. its function.  In the example with the hydraulic motor, that output is torque per hour with certain availability.

By having better monitoring of critical hardware systems and system parameters, failures can be predicted, detected, and avoided before damage occurs. This is of course beneficial for the customer in a traditional sale of hardware products. However, systems for monitoring are of even more importance for the supplier of a function, since the supplier will benefit from delivering agreed upon availability.

The SMART VORTEX project provides support for functional products by applying data stream monitoring and filtering techniques on streams of simulation, and testing data as well as on streams from sensors on industrial equipment in use. This allows the Smart Vortex system to predict potential future errors before they occur, decreasing the risk for unplanned stops and thus significantly increasing hardware and function availability.

To test and verify the SMART VORTEX Suite, a number of application scenarios are defined. In so-called integration sub-projects (ISPs) the components of the SMART VORTEX Suite are applied to specific representative real-world problems at our industrial partner companies. All of these scenarios are inherently collaborative and cross-organizational, as large numbers of engineers, designers, customers, maintenance staff, etc., interact in each individual scenario. By including the industrial partner companies, i.e. the problem owners, in the requirements work, we can assure the industrial relevance of the research and development work, and the quality of the outcomes can be validated in pilot applications also serving as demonstrators for outreach and take-up to produce the critical impact.

Expected Results

In SMART VORTEX, we are aiming at creating a suite of innovative high-impact components. The SMART VORTEX Suite is an infrastructure containing architecture, methods, tools and services for supporting large-scale collaborative engineering projects by intelligent management and analysis of massive data streams to achieve better collaboration and decision making. The architecture describes the data, components and interfaces which can be used to build actual solutions in application scenarios. In addition, we provide a reference implementation of the infrastructure consisting of individual services and tools, which use the architecture and implement the methods developed within SMART VORTEX.