Responsible Knowledge Management in Energy Data Ecosystems

This paper analyzes the challenges and requirements of establishing energy data ecosystems (EDEs) as data-driven infrastructures that overcome the limitations of currently fragmented energy applications. It proposes a new data- and knowledge-driven approach for management and processing. This approach aims to extend the analytics services portfolio of various energy stakeholders and achieve two-way flows of electricity and information for optimized generation, distribution, and electricity consumption. The approach is based on semantic technologies to create knowledge-based systems that will aid machines in integrating and processing resources contextually and intelligently. Thus, a paradigm shift in the energy data value chain is proposed towards transparency and the responsible management of data and knowledge exchanged by the various stakeholders of an energy data space. The approach can contribute to innovative energy management and the adoption of new business models in future energy data spaces

Responsible Knowledge Management in Energy Data Ecosystems

This paper analyzes the challenges and requirements of establishing energy data ecosystems (EDEs) as data-driven infrastructures that overcome the limitations of currently fragmented energy applications. It proposes a new data-and knowledge-driven approach for management and process-ing. This approach aims to extend the analytics services portfolio of various energy stakeholders and achieve two-way flows of electricity and information for optimized generation, distribution, and electricity consumption. The approach is based on semantic technologies to create knowledge-based systems that will aid machines in integrating and processing resources contextually and intelligently. Thus, a paradigm shift in the energy data value chain is proposed towards transparency and the responsible management of data and knowledge exchanged by the various stakeholders of an energy data space. The approach can contribute to innovative energy management and the adoption of new business models in future energy data spaces. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Adaptive Merging on Phase Change Memory

Indexing is a well-known database technique used to facilitate data access and speed up query processing. Nevertheless, the construction and modification of indexes are very expensive. In traditional approaches, all records in the database table are equally covered by the index. It is not effective, since some records may be queried very often and some never. To avoid this problem, adaptive merging has been introduced. The key idea is to create index adaptively and incrementally as a side-product of query processing. As a result, the database table is indexed partially depending on the query workload. This paper faces a problem of adaptive merging for phase change memory (PCM). The most important features of this memory type are: limited write endurance and high write latency. As a consequence, adaptive merging should be investigated from the scratch. We solve this problem in two steps. First, we apply several PCM optimization techniques to the traditional adaptive merging approach. We prove that the proposed method (eAM) outperforms a traditional approach by 60%. After that, we invent the framework for adaptive merging (PAM) and a new PCM-optimized index. It further improves the system performance by 20% for databases where search queries interleave with data modifications

FairNN- Conjoint Learning of Fair Representations for Fair Decisions

In this paper, we propose FairNN a neural network that performs joint feature representation and classification for fairness-aware learning. Our approach optimizes a multi-objective loss function in which (a) learns a fair representation by suppressing protected attributes (b) maintains the information content by minimizing a reconstruction loss and (c) allows for solving a classification task in a fair manner by minimizing the classification error and respecting the equalized odds-based fairness regularized. Our experiments on a variety of datasets demonstrate that such a joint approach is superior to separate treatment of unfairness in representation learning or supervised learning. Additionally, our regularizers can be adaptively weighted to balance the different components of the loss function, thus allowing for a very general framework for conjoint fair representation learning and decision making.Comment: Code will be availabl

Preventing Discriminatory Decision-making in Evolving Data Streams

Bias in machine learning has rightly received significant attention over the last decade. However, most fair machine learning (fair-ML) work to address bias in decision-making systems has focused solely on the offline setting. Despite the wide prevalence of online systems in the real world, work on identifying and correcting bias in the online setting is severely lacking. The unique challenges of the online environment make addressing bias more difficult than in the offline setting. First, Streaming Machine Learning (SML) algorithms must deal with the constantly evolving real-time data stream. Second, they need to adapt to changing data distributions (concept drift) to make accurate predictions on new incoming data. Adding fairness constraints to this already complicated task is not straightforward. In this work, we focus on the challenges of achieving fairness in biased data streams while accounting for the presence of concept drift, accessing one sample at a time. We present Fair Sampling over Stream ($FS^2$), a novel fair rebalancing approach capable of being integrated with SML classification algorithms. Furthermore, we devise the first unified performance-fairness metric, Fairness Bonded Utility (FBU), to evaluate and compare the trade-off between performance and fairness of different bias mitigation methods efficiently. FBU simplifies the comparison of fairness-performance trade-offs of multiple techniques through one unified and intuitive evaluation, allowing model designers to easily choose a technique. Overall, extensive evaluations show our measures surpass those of other fair online techniques previously reported in the literature

Understanding NUMA Effects on Memory Allocation and Reclamation

Memory management in multicore systems is a well studied area. Many approaches to memory management have been developed and tuned with specific hardware architectures in mind, capitalizing on hardware characteristics to improve performance. In this thesis, the focus is on memory allocation and reclamation in multicore systems. I first identify and diagnose a performance anomaly in epoch based memory reclamation (EBR), one of the most popular approaches to reclaiming memory in multicore systems. EBR experiences significant performance degradation when running on multiple processor sockets. This degradation is related to the fact that EBR is vulnerable to thread delays. Even minor delays can trigger a chain reaction that induces longer delays and more substantial performance problems. Moreover, I discover a negative interaction between EBR and popular memory allocators, caused by the fact that EBR frees batches of objects, and these allocators attempt to cache batches of objects for reallocation. The batches freed by EBR frequently overflow the allocator buffers, defeating their purpose and causing substantial performance overhead. To solve these issues, an improvement to EBR, called amortized batch free is introduced to limit the amplification of delays and performance degradation when freeing. Amortized batch free gradually reclaims objects, and can drastically reduce the average time spent freeing an object. This technique is applied to a state of the art EBR algorithms, and significant performance improvements are shown experimentally. This amortized batch freeing technique appears broadly applicable to other memory reclamation algorithms. As a first step in demonstrating this, I also apply it to a simple token based variant of EBR. Token EBR is conceptually simpler and easier to implement than the state of the art EBR algorithm, but has been shown in other work to perform poorly. When the amortized batch free technique is used, Token EBR performs similarly to (and sometimes slightly better than) the state of the art EBR algorithm. Finally, I present a new design for an architecture aware memory allocator for multi-socket systems, using a state of the art allocator called Supermalloc as a starting point for my design. Several key bottlenecks in the original Supermalloc design are improved or eliminated in the new design. In particular, the new design dramatically improves performance when the address space is actively growing, reduces contention on shared resources, and optimizes memory accesses to reduce communication across processor sockets. Taking into account the lessons learned in the study of EBR, the new design also attempts to minimize the overhead of freeing objects. Experiments on a prototype of this new allocator show some performance improvement compared to the original Supermalloc allocator

Automated Deduction – CADE 28

This open access book constitutes the proceeding of the 28th International Conference on Automated Deduction, CADE 28, held virtually in July 2021. The 29 full papers and 7 system descriptions presented together with 2 invited papers were carefully reviewed and selected from 76 submissions. CADE is the major forum for the presentation of research in all aspects of automated deduction, including foundations, applications, implementations, and practical experience. The papers are organized in the following topics: Logical foundations; theory and principles; implementation and application; ATP and AI; and system descriptions