We demonstrate ModsNet , a search tool for pre-trained data science MOD el s recommendatio N using E xamplar da T aset. Given a set of pre-trained data science models, an "example" input dataset, and a user-specified performance metric, ModsNet answers the following query: "what are top-k models that have the best expected performance for the input data?" The need for searching high-quality pre-trained models is evident in data-driven analysis. Inspired by "query by example" paradigm, ModsNet does not require users to write complex queries, but only provide an "examplar" dataset, a task description, and a performance measure as input, and can automatically suggest top- k matching models that are expected to have desirable performance to perform the task over the provided sample dataset. ModsNet utilizes a knowledge graph to integrate model performances over datasets and synchronizes it with a bipartite graph neural network to estimate model performance, reduce inference cost, and promptly respond to top- k model search queries. To cope with strict cold-start (upon receiving a new dataset when no historical performance of registered models are observed), it performs a dynamic, cost-bounded "probe-and-select" strategy to incrementally identify promising models. We demonstrate the application of ModsNet in enabling efficient scientific data analysis.

Data analytical pipelines routinely involve various domain-specific data science models. Such models require expensive manual or training effort and often incur expensive validation costs (e.g., via scientific simulation analysis). Meanwhile, high-value models remain to be ad-hocly created, isolated, and underutilized for a broad community. Searching and accessing proper models for data analysis pipelines is desirable yet challenging for users without domain knowledge. This paper introduces ModsNet, a novel MODel SelectioN framework that only requires an Example daTaset. (1) We investigate the following problem: Given a library of pre-trained models, a limited amount of historical observations of their performance, and an "example" dataset as a query, return k models that are expected to perform the best over the query dataset. (2) We formulate a regression problem and introduce a knowledge-enhanced framework using a model-data interaction graph. Unlike traditional methods, (1) ModsNet uses a dynamic, cost-bounded "probe-and-select" strategy to incrementally identify promising pre-trained models in a strict cold-start scenario (when a new dataset without any interaction with existing models is given). (2) To reduce the learning cost, we develop a clustering-based sparsification strategy to prune unpromising models and their interactions. (3) We showcase of ModsNet built on top of a crowdsourced materials knowledge base platform. Our experiments verified its effectiveness, efficiency, and applications over real-world analytical pipelines.

Modern multidisciplinary materials science routinely processes scientific workflows that integrate different data resources (e.g., X-ray data, scripts, analytical results). Most of such data resources are isolated in research labs, created ad-hocly, and remain underutilized. We demonstrate CRUX, a Crowdsourced platform for materials data ResoUrces and workflow eXploration. CRUX is empowered by coherent data-workflow modeling, knowledge-based resource assembly for workflow search, and data provenance to support workflow exploration. CRUX allows users to declare parameterized workflows as graph patterns, and automatically recommends crowdsourced resources with quality guarantees. We demonstrate the ease-of-use and the performance of CRUX with three categories of queries: data search, workflow recommendation, and resource exploration. We make case of CRUX for peak finding in X-ray Diffraction (XRD) data, a cornerstone task in materials research. We show that CRUX enables new interactive paradigms to explore and design workflows for data analysts in general.