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1. A Demonstration of Interactive Exploration of Big Geospatial Data on UCR-Star

   https://doi.org/10.1145/3397536.3422334  

   Ghosh, Saheli; Sevim, Akil; Eldawy, Ahmed (November 2020, SIGSPATIAL '20: Proceedings of the 28th International Conference on Advances in Geographic Information Systems)

   null (Ed.)

   The ever rising volume of geospatial data is undeniable. So is the need to explore and analyze these datasets. However, these datasets vary widely in their size, coverage, and accuracy. Therefore, users need to assess these aspects of the data to choose the right dataset to use in their analysis. Unfortunately, all the publicly available repositories for geospatial datasets provide a list of datasets with some information about them with no way to explore the datasets beforehand. Through this demonstration, we propose the repository, UCR-Star, that is capable of hosting hundreds of thousands of geospatial datasets that a user can explore visually to judge their quality before even downloading them. This demo provides a deeper dive into the core engine behind UCR-Star. It provides a web interface geared towards database researchers to understand how the index internally works. It provides a comparison interface where the attendees can see side-by-side how two versions of the system work with the ability to customize each of them separately. Finally, the interface reports the response time of the indexes for a quantitative comparison. 

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2. Simple and Automatic Distributed Machine Learning on Ray

   https://doi.org/10.1145/3447548.3470816  

   Zhang, Hao; Li, Zhuohan; Zheng, Lianmin; Stoica, Ion (August 2021, KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining)

   In recent years, the pace of innovations in the fields of machine learning (ML) has accelerated, researchers in SysML have created algorithms and systems that parallelize ML training over multiple devices or computational nodes. As ML models become more structurally complex, many systems have struggled to provide all-round performance on a variety of models. Particularly, ML scale-up is usually underestimated in terms of the amount of knowledge and time required to map from an appropriate distribution strategy to the model. Applying parallel training systems to complex models adds nontrivial development overheads in addition to model prototyping, and often results in lower-than-expected performance. This tutorial identifies research and practical pain points in parallel ML training, and discusses latest development of algorithms and systems on addressing these challenges in both usability and performance. In particular, this tutorial presents a new perspective of unifying seemingly different distributed ML training strategies. Based on it, introduces new techniques and system architectures to simplify and automate ML parallelization. This tutorial is built upon the authors' years' of research and industry experience, comprehensive literature survey, and several latest tutorials and papers published by the authors and peer researchers. The tutorial consists of four parts. The first part will present a landscape of distributed ML training techniques and systems, and highlight the major difficulties faced by real users when writing distributed ML code with big model or big data. The second part dives deep to explain the mainstream training strategies, guided with real use case. By developing a new and unified formulation to represent the seemingly different data- and model- parallel strategies, we describe a set of techniques and algorithms to achieve ML auto-parallelization, and compiler system architectures for auto-generating and exercising parallelization strategies based on models and clusters. The third part of this tutorial exposes a hidden layer of practical pain points in distributed ML training: hyper-parameter tuning and resource allocation, and introduces techniques to improve these aspects. The fourth part is designed as a hands-on coding session, in which we will walk through the audiences on writing distributed training programs in Python, using the various distributed ML tools and interfaces provided by the Ray ecosystem. 

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3. Sparcle: Boosting the Accuracy of Data Cleaning Systems through Spatial Awareness

   https://doi.org/10.14778/3665844.3665862  

   Huang, Yuchuan; Mokbel, Mohamed F (May 2024, Proceedings of the VLDB Endowment)

   Though data cleaning systems have earned great success and wide spread in both academia and industry, they fall short when trying to clean spatial data. The main reason is that state-of-the-art data cleaning systems mainly rely on functional dependency rules where there is sufficient co-occurrence of value pairs to learn that a certain value of an attribute leads to a corresponding value of another attribute. However, for spatial attributes that represent locations, there is very little chance that two records would have the same exact coordinates, and hence co-occurrence is unlikely to exist. This paper presents Sparcle (SPatially-AwaRe CLEaning); a novel framework that injects spatial awareness into the core engine of rule-based data cleaning systems through two main concepts: (1)Spatial Neighborhood, where co-occurrence is relaxed to be within a certain spatial proximity rather than same exact value, and (2)Distance Weighting, where records are given different weights of whether they satisfy a dependency rule, based on their relative distance. Experimental results using a real deployment of Sparcle inside a state-of-the-art data cleaning system, and real and synthetic datasets, show that Sparcle significantly boosts the accuracy of data cleaning systems when dealing with spatial data. 

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4. Search under Uncertainty: Cognitive Biases and Heuristics - Tutorial on Modeling Search Interaction using Behavioral Economics

   https://doi.org/10.1145/3627508.3638297  

   Azzopardi, Leif; Liu, Jiqun (March 2024, ACM)

   Modeling how people interact with search interfaces is core to the field of Interactive Information Retrieval. While various models have been proposed ranging from conceptual (e.g., Belkin’s ASK[12], Berry picking[11], Everyday-life information seeking, etc.) to theoretical (e.g., Information foraging theory[50], Economic theory[4], etc.), more recently there has been a body of working explore how people’s biases and the heuristics that they take influence how they search. This has led to the development of new models of the search process drawing upon Behavioural Economics and Psychology. This half day tutorial will provide a starting point for researchers seeking to learn more about information searching under uncertainty. The tutorial will be structured into two parts. First, we will provide an introduction of the biases and heuristics program put forward by Tversky and Kahneman [59] which assumes that people are not always rational. The second part of the tutorial will provide an overview of the types and space of biases in search [6, 42], before doing a deep dive into several specific examples and the impact of biases on different types of decisions (e.g., health/medical, financial etc.). The tutorial will wrap up with a discussion of some of the practical implication for how we can better design and evaluate IR systems in the light of cognitive biases. 

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5. SWARM: Adaptive Load Balancing in Distributed Streaming Systems for Big Spatial Data

   https://doi.org/10.1145/3460013  

   Daghistani, Anas; Aref, Walid G.; Ghafoor, Arif; Mahmood, Ahmed R. (June 2021, ACM Transactions on Spatial Algorithms and Systems)

   null (Ed.)

   The proliferation of GPS-enabled devices has led to the development of numerous location-based services. These services need to process massive amounts of streamed spatial data in real-time. The current scale of spatial data cannot be handled using centralized systems. This has led to the development of distributed spatial streaming systems. Existing systems are using static spatial partitioning to distribute the workload. In contrast, the real-time streamed spatial data follows non-uniform spatial distributions that are continuously changing over time. Distributed spatial streaming systems need to react to the changes in the distribution of spatial data and queries. This article introduces SWARM, a lightweight adaptivity protocol that continuously monitors the data and query workloads across the distributed processes of the spatial data streaming system and redistributes and rebalances the workloads as soon as performance bottlenecks get detected. SWARM is able to handle multiple query-execution and data-persistence models. A distributed streaming system can directly use SWARM to adaptively rebalance the system’s workload among its machines with minimal changes to the original code of the underlying spatial application. Extensive experimental evaluation using real and synthetic datasets illustrate that, on average, SWARM achieves 2 improvement in throughput over a static grid partitioning that is determined based on observing a limited history of the data and query workloads. Moreover, SWARM reduces execution latency on average 4 compared with the other technique. 

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