000 Allgemeines, Wissenschaft
Refine
Document Type
- Conference proceeding (13)
- Journal article (3)
- Doctoral Thesis (1)
Language
- English (17)
Is part of the Bibliography
- yes (17)
Institute
- Informatik (15)
- ESB Business School (1)
- Technik (1)
Publisher
- IARIA (10)
- Association for Computing Machinery (2)
- Elsevier (2)
Real Time Charging (RTC) applications that reside in the telecommunications domain have the need for extremely fast database transactions. Today´s providers rely mostly on in-memory databases for this kind of information processing. A flexible and modular benchmark suite specifically designed for this domain provides a valuable framework to test the performance of different DB candidates. Besides a data and a load generator, the suite also includes decoupled database connectors and use case components for convenient customization and extension. Such easily produced test results can be used as guidance for choosing a subset of candidates for further tuning/testing and finally evaluating the database most suited to the chosen use cases. This is why our benchmark suite can be of value for choosing databases for RTC use cases.
Distraction of the driver is one of the most frequent causes for car accidents. We aim for a computational cognitive model predicting the driver’s degree of distraction during driving while performing a secondary task, such as talking with co-passengers. The secondary task might cognitively involve the driver to differing degrees depending on the topic of the conversation or the number of co-passengers. In order to detect these subtle differences in everyday driving situations, we aim to analyse in-car audio signals and combine this information with head pose and face tracking information. In the first step, we will assess driving, video and audio parameters reliably predicting cognitive distraction of the driver. These parameters will be used to train the cognitive model in estimating the degree of the driver’s distraction. In the second step, we will train and test the cognitive model during conversations of the driver with co-passengers during active driving. This paper describes the work in progress of our first experiment with preliminary results concerning driving parameters corresponding to the driver’s degree of distraction. In addition, the technical implementation of our experiment combining driving, video and audio data and first methodological results concerning the auditory analysis will be presented. The overall aim for the application of the cognitive distraction model is the development of a mobile user profile computing the individual distraction degree and being applicable also to other systems.
New storage technologies, such as Flash and Non- Volatile Memories, with fundamentally different properties are appearing. Leveraging their performance and endurance requires a redesign of existing architecture and algorithms in modern high performance databases. Multi-Version Concurrency Control (MVCC) approaches in database systems, maintain multiple timestamped versions of a tuple. Once a transaction reads a tuple the database system tracks and returns the respective version eliminating lock-requests. Hence under MVCC reads are never blocked, which leverages well the excellent read performance (high throughput, low latency) of new storage technologies. Upon tuple updates, however, established implementations of MVCC approaches (such as Snapshot Isolation) lead to multiple random writes – caused by (i) creation of the new and (ii) in-place invalidation of the old version – thus generating suboptimal access patterns for the new storage media. The combination of an append based storage manager operating with tuple granularity and snapshot isolation addresses asymmetry and in-place updates. In this paper, we highlight novel aspects of log-based storage, in multi-version database systems on new storage media. We claim that multi-versioning and append-based storage can be used to effectively address asymmetry and endurance. We identify multi-versioning as the approach to address dataplacement in complex memory hierarchies. We focus on: version handling, (physical) version placement, compression and collocation of tuple versions on Flash storage and in complex memory hierarchies. We identify possible read- and cacherelated optimizations.
Strategy to adjust people’s performance capabilities to new requirements and grantee employability in the world of work. Good examples for this are the current changes in the logistics environment. Regularly, new services and processes close to production were taken into the portfolio of logistics enterprises, so the daily Tasks are changing continuously for the skilled works.
LOPEC aims in developing and offering special-tailored training for Lean Logistics and required basic skills for skilled workers on shopfloor level. Needed know-how for today’s challenges in logistics will be transferred. Another aspect of LOPEC is the development and use of a personal excellence self-assessment that allows a Person to assess and thus improve his/her own level of maturity in employability skills. Thus, LOPEC is aiming at People ehancement as entry ticket to lifelong continuous learning by increasing the maturity level of personal logistic excellence. A common European view for “Logistics personal excellence” for skilled workers will ensure that the final product is an open product, using international, pan European validated standards. As results LOPEC will provide training modules for post-secondary education in the area of Lean Logistics, required basics skills and offers transparency of personal excellence with a personal self-assessment Software solution, regarding the personal maturity Level of hard and soft skills at any time. It can be used as an innovative tool for monitoring personal lifelong learning routes as well as within companies as a strategic tool within Human Resource Development.
Managers recognize that software development project teams need to be developed and guided. Although technical skills are necessary, non-technical (NT) skills are equally, if not more, necessary for project success. Currently, there are no proven tools to measure the NT skills of software developers or software development teams. Behavioral markers (observable behaviors that have positive or negative impacts on individual or team performance) are beginning to be successfully used by airline and medical industries to measure NT skill performance. The purpose of this research is to develop and validate the behavior marker system tool that can be used by different managers or coaches to measure the NT skills of software development individuals and teams. This paper presents an empirical study conducted at the Software Factory where users of the behavior marker tool rated video clips of software development teams. The initial results show that the behavior marker tool can be reliably used with minimal training.
This thesis studies concurrency control and composition of transactions in computing environments with long living transactions where local data autonomy of transactions is indispensable. This kind of computing architecture is referred to as a Disconnected System where reads are segregated -disconnected- from writes enabling local data autonomy. Disconnecting reads from writes is inspired by Bertrand Meyer's "Command Query Separation" pattern. This thesis provides a simple yet precise definition for a Disconnected System with a focus on transaction management. Concerning concurrency control, transaction management frameworks implement a'one concurrency control mechanism fits all needs strategy'. This strategy, however, does not consider specific characteristics of data access. The thesis shows the limitations of this strategy if transaction load increases, transactions are long lived, local data autonomy is required, and serializability is aimed at isolation level. For example, in optimistic mechanisms the number of aborts suddenly increases if load increases. In pessimistic mechanisms locking causes long blocking times and is prone to deadlocks. These findings are not new and a common solution used by database vendors is to reduce the isolation. This thesis proposes the usage of a novel approach. It suggests choosing the concurrency control mechanism according to the semantics of data access of a certain data item. As a result a transaction may execute under several concurrency control mechanisms. The idea is to introduce lanes similar to a motorway where each lane is dedicated to a certain class of vehicle with the same characteristics. Whereas disconnecting reads and writes sets the traffic's direction, the semantics of data access defines the lanes. This thesis introduces four concurrency control classes capturing the semantics of data access and each of them has an associated tailored concurrency control mechanism. Class O (the optimistic class) implements a first-committer-wins strategy, class R (the reconciliation class) implements a first-n-committers-win strategy, class P (the pessimistic class) implements a first-reader-wins strategy, and class E (the escrow class) implements a first-n-readers-win strategy. In contrast to solutions that adapt the concurrency control mechanism during runtime, the idea is to classify data during the design phase of the application and adapt the classification only in certain cases at runtime. The result of the thesis is a transaction management framework called O|R|P|E. A performance study based on the TPC-C benchmark shows that O|R|P|E has a better performance and a considerably higher commit rate than other solutions. Moreover, the thesis shows that in O|R|P|E aborts are due to application specific limitations, i.e., constraint violations and not due to serialization conflicts. This is a result of considering the semantics.
This paper presents a concurrency control mechanism that does not follow a ‘one concurrency control mechanism fits all needs’ strategy. With the presented mechanism a transaction runs under several concurrency control mechanisms and the appropriate one is chosen based on the accessed data. For this purpose, the data is divided into four classes based on its access type and usage (semantics). Class O (the optimistic class) implements a first-committer-wins strategy, class R (the reconciliation class) implements a first-n-committers-win strategy, class P (the pessimistic class) implements a first reader-wins strategy, and class E (the escrow class) implements a firsnreaderswin strategy. Accordingly, the model is called OjRjPjE. Under this model the TPC-C benchmark outperforms other CC mechanisms like optimistic Snapshot Isolation.
This work presents a disconnected transaction model able to cope with the increased complexity of longliving, hierarchically structured, and disconnected transactions. Wecombine an Open and Closed Nested Transaction Model with Optimistic Concurrency Control and interrelate flat transactions with the aforementioned complex nature. Despite temporary inconsistencies during a transaction’s execution our model ensures consistency.
The recent years and especially the Internet have changed the way on how data is stored. We now often store data together with its creation time-stamp. These data sequences potentially enable us to track the change of data over time. This is quite interesting, especially in the e-commerce area, in which classification of a sequence of customer actions, is still a challenging task for data miners. However, before Standard algorithms such as Decision Trees, Neuronal Nets, Naive Bayes or Bayesian Belief Networks can be applied on sequential data, preparations need to be done in order to capture the information stored within the sequences. Therefore, this work presents a systematic approach on how to reveal sequence patterns among data and how to construct powerful features out of the primitive sequence attributes. This is achieved by sequence aggregation and the incorporation of time dimension into the Feature construction step. The proposed algorithm is described in detail and applied on a real life data set, which demonstrates the ability of the proposed algorithm to boost the classification performance of well known data mining algorithms for classification tasks.
Knowledge transfer is very important to our knowledge-based society and many approaches have been proposed to describe this transfer. However, these approaches take a rather abstract view on knowledge transfer, which makes implementation difficult. In order to address this issue, we introduce a layered model for knowledge transfer that structures the individual steps of knowledge transfer in more detail. This paper gives a description of the process and also an example of the application of the layered model for knowledge transfer. The example is located in the area of business process modelling. Business processes contain the important knowledge describing the procedures of the company to produce products and services. Knowledge transfer is the fundamental basis in the modelling and usage of Business processes, which makes it an interesting use case for the layered model for knowledge transfer.