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1. LensKit for Python: Next-Generation Software for Recommender Systems Experiments

   https://doi.org/10.1145/3340531.3412778  

   Ekstrand, Michael D. (October 2020, Proceedings of the 29th ACM International Conference on Information and Knowledge Management)

   null (Ed.)

   LensKit is an open-source toolkit for building, researching, and learning about recommender systems. First released in 2010 as a Java framework, it has supported diverse published research, small-scale production deployments, and education in both MOOC and traditional classroom settings. In this paper, I present the next generation of the LensKit project, re-envisioning the original tool's objectives as flexible Python package for supporting recommender systems research and development. LensKit for Python (LKPY) enables researchers and students to build robust, flexible, and reproducible experiments that make use of the large and growing PyData and Scientific Python ecosystem, including scikit-learn, and TensorFlow. To that end, it provides classical collaborative filtering implementations, recommender system evaluation metrics, data preparation routines, and tools for efficiently batch running recommendation algorithms, all usable in any combination with each other or with other Python software. This paper describes the design goals, use cases, and capabilities of LKPY, contextualized in a reflection on the successes and failures of the original LensKit for Java software. 

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2. AFrame: Extending DataFrames for Large-Scale Modern Data Analysis

   https://doi.org/10.1109/BigData47090.2019.9006303  

   Sinthong, Phanwadee; Carey, Michael J. (December 2019, 2019 IEEE International Conference on Big Data (Big Data))

   Analyzing the increasingly large volumes of data that are available today, possibly including the application of custom machine learning models, requires the utilization of distributed frameworks. This can result in serious productivity issues for “normal” data scientists. This paper introduces AFrame, a new scalable data analysis package powered by a Big Data management system that extends the data scientists' familiar DataFrame operations to efficiently operate on managed data at scale. AFrame is implemented as a layer on top of Apache AsterixDB, transparently scaling out the execution of DataFrame operations and machine learning model invocation through a parallel, shared-nothing big data management system. AFrame incrementally constructs SQL++ queries and leverages AsterixDB's semistructured data management facilities, user-defined function support, and live data ingestion support. In order to evaluate the proposed approach, this paper also introduces an extensible micro-benchmark for use in evaluating DataFrame performance in both single-node and distributed settings via a collection of representative analytic operations. This paper presents the architecture of AFrame, describes the underlying capabilities of AsterixDB that efficiently support modern data analytic operations, and utilizes the proposed benchmark to evaluate and compare the performance and support for largescale data analyses provided by alternative DataFrame libraries. 

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3. Towards a Memory-Adaptive Hybrid Hash Join Design

   https://doi.org/10.1109/BigData59044.2023.10386098  

   Siviero, Giulliano_Silva Zanotti; Jahangiri, Shiva (December 2023, 2023 IEEE International Conference on Big Data (BigData))

   In database management systems (DBMSs) that handle multiple concurrent queries, adapting to fluctuating workloads is crucial. This flexibility allows the DBMS to revise decisions based on current workload and available resources. As memory availability changes with the arrival or completion of queries, having memory-intensive operators like the Hybrid Hash Join that dynamically adapt is vital. This paper introduces a new memory-adaptive Hash-Based join algorithm design implemented in Apache AsterixDB and evaluates its responsiveness to memory variability. 

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4. PolyFrame: a retargetable query-based approach to scaling dataframes

   https://doi.org/10.14778/3476249.3476281  

   Sinthong, Phanwadee; Carey, Michael J. (July 2021, Proceedings of the VLDB Endowment)

   null (Ed.)

   In the last few years, the field of data science has been growing rapidly as various businesses have adopted statistical and machine learning techniques to empower their decision-making and applications. Scaling data analyses to large volumes of data requires the utilization of distributed frameworks. This can lead to serious technical challenges for data analysts and reduce their productivity. AFrame, a data analytics library, is implemented as a layer on top of Apache AsterixDB, addressing these issues by providing the data scientists' familiar interface, Pandas Dataframe, and transparently scaling out the evaluation of analytical operations through a Big Data management system. While AFrame is able to leverage data management facilities (e.g., indexes and query optimization) and allows users to interact with a large volume of data, the initial version only generated SQL++ queries and only operated against AsterixDB. In this work, we describe a new design that retargets AFrame's incremental query formation to other query-based database systems, making it more flexible for deployment against other data management systems with composable query languages. 

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5. How to make your results reproducible with UCR-star and spider

   https://doi.org/10.1145/3557994.3565975  

   Majumder, Tomal; Eldawy, Ahmed (November 2022, the 4th ACM SIGSPATIAL International Workshop on APIs and Libraries for Geospatial Data Science)

   With the rise of data science, there has been a sharp increase in data-driven techniques that rely on both real and synthetic data. At the same time, there is a growing interest from the scientific com- munity in the reproducibility of results. Some conferences include this explicitly in their review forms or give special badges to repro- ducible papers. This tutorial describes two systems that facilitate the design of reproducible experiments on both real and synthetic data. UCR-Star is an interactive repository that hosts terabytes of open geospatial data. In addition to the ability to explore and visu- alize this data, UCR-Star makes it easy to share all or parts of these datasets in many standard formats ensuring that other researchers can get the same exact data mentioned in the paper. Spider is a spa- tial data generator that generates standardized spatial datasets with full control over the data characteristics which further promotes the reproducibility of results. This tutorial will be organized into two parts. The first part will exhibit the key features of UCR-star and Spider where participants can get hands-on experience in in- teracting with real spatial datasets, generating synthetic data with varying distributions, and downloading them to a local machine or a remote server. The second part will explore the integration of both UCR-Star and Spider into existing systems such as QGIS and Apache AsterixDB. 

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