Proceedings of the 7th ACM international conference on Distributed event-based systems
Bibliographic record
Abstract
It is our great pleasure to welcome you to the 7th ACM International Conference on Distributed Event-Based Systems (DEBS 2013) here at The University of Texas at Arlington, Arlington, Texas, USA. DEBS is the flagship conference for the dissemination of original research, demonstration of prototypes, the discussion of new practical insights, and the reporting of relevant experience relating to event-based computing. Event-based systems have gained in importance in many application domains, ranging from real-time data processing in web environments, nontraditional applications, such as railroad safety and track monitoring, logistics and networking, to complex event processing in finance and security. The event-based paradigm strengthened by continuous stream data processing has gathered momentum as witnessed by current efforts in areas such as event-driven architectures, big data systems, the internet of things, complex event processing, publish/subscribe systems, business process management, cloud computing, web services, information dissemination, and message-oriented middleware. The DEBS conference brings researchers, students, and practitioners from these various communities together in an international setting to exchange ideas and knowledge about current research work and open challenges. The conference also provides a forum for facilitating the exchange of ideas between academics, vendors, and application developers. The call for scientific papers attracted 58 submissions from Asia, Canada, Europe, and the United States. The program committee accepted 16 papers that cover a variety of topics, including distributed stream processing, publish/subscribe systems, complex event processing models and languages, and mobility and query optimization. The technical program is complemented by three keynotes talks provided by Roger Barga (Microsoft Research), David Wollman (National Institute of Standards and Technology), and Shailendra Mishra (Paypal). Roger Barga addresses the need for batch-oriented analytics engines that are supported by storage and data processing engines such as Hadoop to also provide real-time analytics capabilities. David Wollman outlines the role of stream and event processing for the Smart Grid and other cyber-physical systems applications. Finally, Shailendra Mishra describes the challenges of complex event processing as a supporting technology for the data cloud and cloud services framework. In addition, Jennifer Maxwell (BNSF Railway) presents an invited experience report on the use of event-based technology for the development of an advanced railroad application. To place emphasis on the practical use of event-based technologies in distributed environments, the Grand Challenge competition provides a showcase of event-based solutions to problems that are relevant to industry at large using real-life data and queries. This year's challenge involved demonstrating the applicability of event-based systems for real-time analytics over high velocity sensor data collected from a soccer game. In addition to the Grand Challenge competition, demonstration and poster sessions provide an opportunity for groups of students, researchers, and practitioners to showcase their prototypes and research for an international audience. The Doctoral Workshop also acts as a meeting place for students to discuss their research and obtain meaningful feedback as well as interaction with experts in the field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.090 | 0.037 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".