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Physical Sciences and Mathematics Commons™

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Databases and Information Systems

2021

University of Massachusetts Amherst

Articles 1 - 8 of 8

Full-Text Articles in Physical Sciences and Mathematics

Enhancing Usability And Explainability Of Data Systems, Anna Fariha Oct 2021

Enhancing Usability And Explainability Of Data Systems, Anna Fariha

Doctoral Dissertations

The recent growth of data science expanded its reach to an ever-growing user base of nonexperts, increasing the need for usability, understandability, and explainability in these systems. Enhancing usability makes data systems accessible to people with different skills and backgrounds alike, leading to democratization of data systems. Furthermore, proper understanding of data and data-driven systems is necessary for the users to trust the function of the systems that learn from data. Finally, data systems should be transparent: when a data system behaves unexpectedly or malfunctions, the users deserve proper explanation of what caused the observed incident. Unfortunately, …

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History Modeling For Conversational Information Retrieval, Chen Qu Oct 2021

History Modeling For Conversational Information Retrieval, Chen Qu

Doctoral Dissertations

Conversational search is an embodiment of an iterative and interactive approach to information retrieval (IR) that has been studied for decades. Due to the recent rise of intelligent personal assistants, such as Siri, Alexa, AliMe, Cortana, and Google Assistant, a growing part of the population is moving their information-seeking activities to voice- or text-based conversational interfaces. One of the major challenges of conversational search is to leverage the conversation history to understand and fulfill the users' information needs. In this dissertation work, we investigate history modeling approaches for conversational information retrieval. We start from history modeling for user intent prediction. …

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Enabling Declarative And Scalable Prescriptive Analytics In Relational Data, Matteo Brucato Oct 2021

Enabling Declarative And Scalable Prescriptive Analytics In Relational Data, Matteo Brucato

Doctoral Dissertations

Constrained optimization problems are at the heart of significant applications in a broad range of domains, including finance, transportation, manufacturing, and healthcare. They are often found at the final step of business analytics, namely prescriptive analytics, to allow businesses to transform a rich understanding of data, typically provided by advanced predictive models, into actionable decisions. Modeling and solving these problems has relied on application-specific solutions, which are often complex, error-prone, and do not generalize. Our goal is to create a domain-independent, declarative approach, supported and powered by the system where the data relevant to these problems typically resides: the database. …

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Neural Approaches To Feedback In Information Retrieval, Keping Bi Oct 2021

Neural Approaches To Feedback In Information Retrieval, Keping Bi

Doctoral Dissertations

Relevance feedback on search results indicates users' search intent and preferences. Extensive studies have shown that incorporating relevance feedback (RF) on the top k (usually 10) ranked results significantly improves the performance of re-ranking. However, most existing research on user feedback focuses on words-based retrieval models. Recently, neural retrieval models have shown their efficacy in capturing relevance matching in retrieval but little research has been conducted on neural approaches to feedback. This leads us to study different aspects of feedback with neural approaches in the dissertation. RF techniques are seldom used in real search scenarios since they can require significant …

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Towards Practical Differentially Private Mechanism Design And Deployment, Dan Zhang Jul 2021

Towards Practical Differentially Private Mechanism Design And Deployment, Dan Zhang

Doctoral Dissertations

As the collection of personal data has increased, many institutions face an urgent need for reliable protection of sensitive data. Among the emerging privacy protection mechanisms, differential privacy offers a persuasive and provable assurance to individuals and has become the dominant model in the research community. However, despite growing adoption, the complexity of designing differentially private algorithms and effectively deploying them in real-world applications remains high. In this thesis, we address two main questions: 1) how can we aid programmers in developing private programs with high utility? and 2) how can we deploy differentially private algorithms to visual analytics systems? …

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Neural Methods For Answer Passage Retrieval Over Sparse Collections, Daniel Cohen Apr 2021

Neural Methods For Answer Passage Retrieval Over Sparse Collections, Daniel Cohen

Doctoral Dissertations

Recent advances in machine learning have allowed information retrieval (IR) techniques to advance beyond the stage of handcrafting domain specific features. Specifically, deep neural models incorporate varying levels of features to learn whether a document answers the information need of a query. However, these neural models rely on a large number of parameters to successfully learn a relation between a query and a relevant document.

This reliance on a large number of parameters, combined with the current methods of optimization relying on small updates necessitates numerous samples to allow the neural model to converge on an effective relevance function. This …

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Quantifying The Impact Of Non-Stationarity In Reinforcement Learning-Based Traffic Signal Control, Lucas N. Alegre, Ana L.C. Bazzan, Bruno C. Da Silva Jan 2021

Quantifying The Impact Of Non-Stationarity In Reinforcement Learning-Based Traffic Signal Control, Lucas N. Alegre, Ana L.C. Bazzan, Bruno C. Da Silva

Computer Science Department Faculty Publication Series

In reinforcement learning (RL), dealing with non-stationarity is a challenging issue. However, some domains such as traffic optimization are inherently non-stationary. Causes for and effects of this are manifold. In particular, when dealing with traffic signal controls, addressing non-stationarity is key since traffic conditions change over time and as a function of traffic control decisions taken in other parts of a network. In this paper we analyze the effects that different sources of non-stationarity have in a network of traffic signals, in which each signal is modeled as a learning agent. More precisely, we study both the effects of changing …

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Cloud And Edge Computation Offloading For Latency Limited Services, Ivana Kovacevic, Erkki Harjula, Savo Glisic, Beatriz Lorenzo, Mika Ylianttila Jan 2021

Cloud And Edge Computation Offloading For Latency Limited Services, Ivana Kovacevic, Erkki Harjula, Savo Glisic, Beatriz Lorenzo, Mika Ylianttila

Electrical and Computer Engineering Faculty Publication Series

Multi-access Edge Computing (MEC) is recognised as a solution in future networks to offload computation and data storage from mobile and IoT devices to the servers at the edge of mobile networks. It reduces the network traffic and service latency compared to passing all data to cloud data centers while offering greater processing power than handling tasks locally at terminals. Since MEC servers are scattered throughout the radio access network, their computation capacities are modest in comparison to large cloud data centers. Therefore, offloading decision between MEC and cloud server should minimize the usage of the resources while maximizing the …

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