Internet of Things applications analyze the data coming from large networks of sensor devices using relational and signal processing operations and running the same query logic over groups of sensor signals. To support such increasingly important scenarios, many ata management systems integrate with numerical frameworks like R. Such solutions, however, incur significant performance penalties as relational data processing engines and numerical tools operate on fundamentally different data models with expensive intercommunication mechanisms. In addition, none of these solutions support efficient real-time and incremental analysis. In this paper, we advocate a deep integration of signal processing operations and general-purpose query processors. We aim to reconcile the disparate data models and provide a common query language that allows users to seamlessly interleave tempo-relational and signal operations for both online and offline processing. Our approach is extensible and offers frameworks for quick and easy integration of user-defined operations while supporting incremental computation. Our system that deeply integrates relational and signal operations, called TrillDSP, achieves up to two orders of magnitude better performance than popular loosely-coupled data management systems on grouped signal processing workflows.<\/p>\n","protected":false},"excerpt":{"rendered":"
Internet of Things applications analyze the data coming from large networks of sensor devices using relational and signal processing operations and running the same query logic over groups of sensor signals. To support such increasingly important scenarios, many ata management systems integrate with numerical frameworks like R. Such solutions, however, incur significant performance penalties as […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"ACM","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"SIGMOD 2017, May 14-19, 2017, Chicago, Illinois, USA","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"SIGMOD 2017, May 14-19, 2017, Chicago, Illinois, USA","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"","msr_other_contributors":"","msr_speaker":"","msr_award":"","msr_affiliation":"","msr_institution":"","msr_host":"","msr_version":"","msr_duration":"","msr_original_fields_of_study":"","msr_release_tracker_id":"","msr_s2_match_type":"","msr_citation_count_updated":"","msr_published_date":"2017-05-14","msr_highlight_text":"","msr_notes":"","msr_longbiography":"","msr_publicationurl":"","msr_external_url":"","msr_secondary_video_url":"","msr_conference_url":"","msr_journal_url":"","msr_s2_pdf_url":"","msr_year":0,"msr_citation_count":0,"msr_influential_citations":0,"msr_reference_count":0,"msr_s2_match_confidence":0,"msr_microsoftintellectualproperty":true,"msr_s2_open_access":false,"msr_s2_author_ids":[],"msr_pub_ids":[],"msr_hide_image_in_river":0,"footnotes":""},"msr-research-highlight":[],"research-area":[13563],"msr-publication-type":[193716],"msr-publisher":[],"msr-focus-area":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-480456","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-data-platform-analytics","msr-locale-en_us"],"msr_publishername":"ACM","msr_edition":"SIGMOD 2017, May 14-19, 2017, Chicago, Illinois, USA","msr_affiliation":"","msr_published_date":"2017-05-14","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"480453","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"trilldsp-sigmod17","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2018\/04\/trilldsp-sigmod17.pdf","id":480453,"label_id":0}],"msr_related_uploader":"","msr_citation_count":0,"msr_citation_count_updated":"","msr_s2_paper_id":"","msr_influential_citations":0,"msr_reference_count":0,"msr_arxiv_id":"","msr_s2_author_ids":[],"msr_s2_open_access":false,"msr_s2_pdf_url":null,"msr_attachments":[],"msr-author-ordering":[{"type":"text","value":"Milos Nikolic","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Badrish Chandramouli","user_id":31166,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Badrish Chandramouli"},{"type":"user_nicename","value":"Jonathan Goldstein","user_id":32389,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Jonathan Goldstein"}],"msr_impact_theme":[],"msr_research_lab":[199565],"msr_event":[],"msr_group":[957177],"msr_project":[171207,170875],"publication":[],"video":[],"msr-tool":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":171207,"post_title":"Trill","post_name":"trill","post_type":"msr-project","post_date":"2013-09-19 14:35:28","post_modified":"2019-07-16 08:47:44","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/trill\/","post_excerpt":"Trill is a high-performance open-source in-memory incremental analytics library. 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