GreedyDual-Join: Locality-Aware Buffer Management for Approximate Join Processing Over Data Streams

We investigate adaptive buffer management techniques for approximate evaluation of sliding window joins over multiple data streams. In many applications, data stream processing systems have limited memory or have to deal with very high speed data streams. In both cases, computing the exact results of joins between these streams may not be feasible, mainly because the buffers used to compute the joins contain much smaller number of tuples than the tuples contained in the sliding windows. Therefore, a stream buffer management policy is needed in that case. We show that the buffer replacement policy is an important determinant of the quality of the produced results. To that end, we propose GreedyDual-Join (GDJ) an adaptive and locality-aware buffering technique for managing these buffers. GDJ exploits the temporal correlations (at both long and short time scales), which we found to be prevalent in many real data streams. We note that our algorithm is readily applicable to multiple data streams and multiple joins and requires almost no additional system resources. We report results of an experimental study using both synthetic and real-world data sets. Our results demonstrate the superiority and flexibility of our approach when contrasted to other recently proposed techniques

An Integrated Multi-Time-Scale Modeling for Solar Irradiance Forecasting Using Deep Learning

For short-term solar irradiance forecasting, the traditional point forecasting methods are rendered less useful due to the non-stationary characteristic of solar power. The amount of operating reserves required to maintain reliable operation of the electric grid rises due to the variability of solar energy. The higher the uncertainty in the generation, the greater the operating-reserve requirements, which translates to an increased cost of operation. In this research work, we propose a unified architecture for multi-time-scale predictions for intra-day solar irradiance forecasting using recurrent neural networks (RNN) and long-short-term memory networks (LSTMs). This paper also lays out a framework for extending this modeling approach to intra-hour forecasting horizons thus, making it a multi-time-horizon forecasting approach, capable of predicting intra-hour as well as intra-day solar irradiance. We develop an end-to-end pipeline to effectuate the proposed architecture. The performance of the prediction model is tested and validated by the methodical implementation. The robustness of the approach is demonstrated with case studies conducted for geographically scattered sites across the United States. The predictions demonstrate that our proposed unified architecture-based approach is effective for multi-time-scale solar forecasts and achieves a lower root-mean-square prediction error when benchmarked against the best-performing methods documented in the literature that use separate models for each time-scale during the day. Our proposed method results in a 71.5% reduction in the mean RMSE averaged across all the test sites compared to the ML-based best-performing method reported in the literature. Additionally, the proposed method enables multi-time-horizon forecasts with real-time inputs, which have a significant potential for practical industry applications in the evolving grid.Comment: 19 pages, 12 figures, 3 tables, under review for journal submissio

Individual data analysis and Unified Theories of Cognition: A methodological proposal

Unified theories regularly appear in psychology. They also regularly fail to fulfil all of their goals. Newell (1990) called for their revival, using computer modelling as a way to avoid the pitfalls of previous attempts. His call, embodied in the Soar project has so far, however, failed to produce the breakthrough it promised. One of the reasons for the lack of success of Newell’s approach is that the methodology commonly used in psychology, based on controlling potentially confounding variables by using group data, is not the best way forward for developing unified theories of cognition. Instead, we propose an approach where (a) the problems related to group averages are alleviated by analysing subjects individually; (b) there is a close interaction between theory building and experimentation; and (c) computer technology is used to routinely test versions of the theory on a wide range of data. The advantages of this approach heavily outweigh the disadvantages

Models of verbal working memory capacity: What does it take to make them work?

Theories of working memory (WM) capacity limits will be more useful when we know what aspects of performance are governed by the limits and what aspects are governed by other memory mechanisms. Whereas considerable progress has been made on models of WM capacity limits for visual arrays of separate objects, less progress has been made in understanding verbal materials, especially when words are mentally combined to form multiword units or chunks. Toward a more comprehensive theory of capacity limits, we examined models of forced-choice recognition of words within printed lists, using materials designed to produce multiword chunks in memory (e.g., leather brief case). Several simple models were tested against data from a variety of list lengths and potential chunk sizes, with test conditions that only imperfectly elicited the interword associations. According to the most successful model, participants retained about 3 chunks on average in a capacity-limited region of WM, with some chunks being only subsets of the presented associative information (e.g., leather brief case retained with leather as one chunk and brief case as another). The addition to the model of an activated long-term memory component unlimited in capacity was needed. A fixed-capacity limit appears critical to account for immediate verbal recognition and other forms of WM. We advance a model-based approach that allows capacity to be assessed despite other important processing contributions. Starting with a psychological-process model of WM capacity developed to understand visual arrays, we arrive at a more unified and complete model

A Unified Multilingual Handwriting Recognition System using multigrams sub-lexical units

We address the design of a unified multilingual system for handwriting recognition. Most of multi- lingual systems rests on specialized models that are trained on a single language and one of them is selected at test time. While some recognition systems are based on a unified optical model, dealing with a unified language model remains a major issue, as traditional language models are generally trained on corpora composed of large word lexicons per language. Here, we bring a solution by con- sidering language models based on sub-lexical units, called multigrams. Dealing with multigrams strongly reduces the lexicon size and thus decreases the language model complexity. This makes pos- sible the design of an end-to-end unified multilingual recognition system where both a single optical model and a single language model are trained on all the languages. We discuss the impact of the language unification on each model and show that our system reaches state-of-the-art methods perfor- mance with a strong reduction of the complexity.Comment: preprin

Discrimination nets, production systems and semantic networks: Elements of a unified framework

A number of formalisms have been used in cognitive science to account for cognition in general and learning in particular. While this variety denotes a healthy state of theoretical development, it somewhat hampers communication between researchers championing different approaches and makes comparison between theories difficult. In addition, it has the consequence that researchers tend to study cognitive phenomena best suited to their favorite formalism. It is therefore desirable to propose frameworks which span traditional formalisms. In this paper, we pursue two goals: first, to show how three (symbolic) formalisms widely used in theorizing about and in simulating human cognition—discrimination nets, semantic networks and production systems—may be used in a single, conceptually unified framework; and second to show how this framework can be used to develop a comprehensive theory of learning. Within this theory, learning is construed as (a) developing perceptual and conceptual discrimination nets, (b) adding semantic links, and (c) creating productions. We start by giving a brief description of each of these formalisms; we then describe a theoretical framework that incorporates the three formalisms, and show how these may coexist. Throughout this description, examples from chess, a highly studied field of expertise and a classical object of study in cognitive science, will be provided. These examples will illustrate how the framework can be worked out into a more detailed cognitive theory. Finally, we draw some theoretical consequences of the framework proposed here