THE NEED FOR QUALITY The first thing that we need to consider, in any organization, is that quality is the most important thing. The quality of your work defines you.  Whoever you are,  Whatever you do,  I can find the same products and services cheaper somewhere else. But your quality is your signature. Developing and delivering high quality products and services means that you are doing things correctly from the beginning. As a consequence, you are reducing the need for additional services, from verification to warranty.

Total quality management.

House of Quality

pdf/scheduling-parallel-and-distributed-processing-for-4axor8ujuz.pdf

Scheduling parallel and distributed processing for automotive data stream management system

The consequences of traffic accidents are not only limited to road travellers (congestion, delays), but may also affect the area surrounding the incident, for instance, in the case of incidents involving release of chemical substances. A lack of a real-time assessment of the mobility consequences of an incident as well as of its consequences for the surrounding area hampers the decision makers’ ability to respond effectively to an incident and to manage its consequences. The main purpose of this paper is to provide a full picture of the mobility and area consequences of an incident in near real time by creating situation awareness for Incident Management actors. In particular, the study focuses on situation awareness for (1) mobility and how it is affected by an incident, (2) for the area surrounding the incident, and (3) for the site accessibility. The paper addresses information obtained from locationbased services and pays particular attention to GSM data obtained from cellular phone networks. The advantages and limitations of the process of retrieving location information and proper transportation parameters from cellular phone

/pdf/the-use-of-gsm-data-for-transport-safety-management-an-19xu6anm7v.pdf

The use of GSM data for transport safety management: An exploratory review

Maintenance of assembly and manufacturing equipment is crucial to ensure productivity, product quality, on-time delivery, and a safe working environment. Predictive Maintenance approach utilizes the condition monitoring (CM) data to predict the future machine conditions and makes decisions upon this prediction. Recent development in CM leads to context aware approach where in parallel with CM measurements also data and information related to the context are gathered. Context could be operational condition, history of machine usage and performed maintenance actions. In general more obtained information gives better accuracy of prediction. It is important to track operational context in dynamically changing environment. Today in manufacturing we can observe shift from mass production to mass customisation. This leads to changes from long series of identical products to short series of different variants. Therefore implies changing operational conditions for manufacturing equipment. Moreover, where asset consist of multiple identical or similar equipment the context aware method can be used to combine in reliable way information. This should allow to increase accuracy of prediction for population as a whole as well as for each equipment instances. Same of those data have been already recorded and stored in industrial IT systems. However, it is distributed over different IT systems that are used by different functional units (e.g. maintenance department, production department, quality department, tooling department etc.). This paper is a conceptual paper based on initial research work and investigation in two manufacturing companies from automotive industry.

Context Awareness in Predictive Maintenance

One of the main challenges in the development of traffic systems is to assure safety for all road users. Hence, especially expensive vehicles are equipped with advanced driver assistance systems (ADAS) that use data about the vehicle and information about objects in the proximity of the vehicle to execute the assistance function. These objects have to be detected by sensors and they have to be tracked over multiple scans to keep the object's state up-to-date. Usually, such ADAS are developed as proprietary systems that are tailored for the specific assistance function and the specific sensors in use. Indeed, that leads to a very efficient system. However, changing system properties, e. g. an exchange of sensors, is very expensive. In this case, very often at least some parts of the system code have to be reimplemented. To solve this problem of bad maintainability which arises especially during the development of new assistance functions in this work a new architecture for ADAS is presented. The relevant information for the assistance function is no longer provided by hard coded, predefined processes, but by flexible continuous operator plans in a datastream management system. These operator plans build up a dynamic context model of the vehicle's environment. The context model is kept up-to-date by object tracking operators in these operator plans and is then used as a data source to extract information for different assistance functions. This extraction is also done by operator plans that produce only relevant information and discard other information.

StreamCars: A new flexible architecture for driver assistance systems

In this paper, we describe the requirements and benefits for integrating data stream processing with database management systems. Currently, these technologies focus on very different tasks; streams systems extract instances of patterns from streams of transient data, while database systems store, manage, provide access to, and analyze persistent data. Many applications, e.g., patient care, program trading, or flight supervision, however, depend on the functionality and operational characteristics of both types of systems. We discuss how to design a federated system which provides the benefits of both approaches.

DBMS meets DSMS - Towards a Federated Solution.

Context-aware applications adapt their behavior according to the current situation of their user or their (often physical) environment. This adaptation could be the change of the user interface, the performance of actions (like sending messages or triggering actuators), or the change of used resources (like network bandwidth or processing power). To determine relevant situations, many heterogeneous data sources could be used, ranging from sensor data over mined patterns in files to explicit user input. Since most sensors are not perfect, context quality has to be considered. And since many context-aware applications are mobile, the set of data sources might change during runtime. All this issues make context management and reasoning, and the development of correct adaptations within context-aware applications a challenging task. This paper is based on a keynote given at ICSOFT 2012 that introduces a layered model to separate different tasks and concerns in designing data models for context-aware applications. It shows how existing works map to this layered model, and how the model can help in designing context aware applications that are better to maintain and safer to use.

Data, Context, Situation: On the Usefulness of Semantic Layers for Designing Context-Aware Systems

Issues in Combined Static and Dynamic Data Management.

There are many examples for context-aware applications, which adapt their behavior according to their situation: navigation systems that change the proposed routing based on current traffic information, enhanced coffee mugs that detect spontaneous coffee breaks and inform the other colleagues, or, more seriously, smart production environments that improve the efficiency of technical processes by up-to-date environmental information, to name a few. Another example is the educational domain, driven by the vision of the pervasive university, where innovative applications help students and teachers to communicate, disseminate and assimilate knowledge, and create documentation in a much more flexible and situational adaptive way. The design of this class of applications is difficult since the behavior depends on changing situations that have to be derived from lower level context information (e.g., sensor data), or given by the user. A commonly used context definition by Dey [Dey01] states that context is “any information that can be used to characterize the situation of entities”. This shows the great variety of potential context information.

Managing context information – A key technology for pervasive computing

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