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The recent advances in the automation of metadata normalization and the invention of a unified schema --- Brick --- alleviate the metadata normalization challenge for deploying portable applications across buildings. Yet, the lack of compatibility between existing metadata normalization methods precludes the possibility of comparing and combining them. While generic machine learning (ML) frameworks, such as MLJAR and OpenML, provide versatile interfaces for standard ML problems, they cannot easily accommodate the metadata normalization tasks for buildings due to the heterogeneity in the inference scope, type of data required as input, evaluation metric, and the building-specific human-in-the-loop learning procedure. We propose Plaster, an open and modular framework that incorporates existing advances in building metadata normalization. It provides unified programming interfaces for various types of learning methods for metadata normalization and defines standardized data models for building metadata and timeseries data. Thus, it enables the integration of different methods via a workflow, benchmarking of different methods via unified interfaces, and rapid prototyping of new algorithms. With Plaster, we 1) show three examples of the workflow integration, delivering better performance than individual algorithms, 2) benchmark/analyze five algorithms over five common buildings, and 3) exemplify the process of developing a new algorithm involving time series features. We believe Plaster will facilitate the development of new algorithms and expedite the adoption of standard metadata schema such as Brick, in order to enable seamless smart building applications in the future. 

Today large amount of data is generated by cities. Many of the datasets are openly available and are contributed by different sectors, government bodies and institutions. The new data can affect our understanding of the issues faced by cities and can support evidence based policies. However usage of data is limited due to difficulty in assimilating data from different sources. Open datasets often lack uniform structure which limits its analysis using traditional database systems. In this paper we present Citadel, a data hub for cities. Citadel's goal is to support end to end knowledge discovery cyber-infrastructure for effective analysis and policy support. Citadel is designed to ingest large amount of heterogeneous data and supports multiple use cases by encouraging data sharing in cities. Our poster presents the proposed features, architecture, implementation details and initial results. 

Commercial buildings have long since been a primary target for applications from a number of areas: from cyber-physical systems to building energy use to improved human interactions in built environments. While technological advances have been made in these areas, such solutions rarely experience widespread adoption due to the lack of a common descriptive schema which would reduce the now-prohibitive cost of porting these applications and systems to different buildings. Recent attempts have sought to address this issue through data standards and metadata schemes, but fail to capture the set of relationships and entities required by real applications. Building upon these works, this paper describes Brick, a uniform schema for representing metadata in buildings. Our schema defines a concrete ontology for sensors, subsystems and relationships among them, which enables portable applications. We demonstrate the completeness and effectiveness of Brick by using it to represent the entire vendor-specific sensor metadata of six diverse buildings across different campuses, comprising 17,700 data points, and running eight complex unmodified applications on these buildings.