Event processing, analysis, and visualization are increasingly important, and common, problems as telemetry and log recording become ubiquitous. We are still learning about the space of ways to both query and see the results of sequential queries against event-logged data. In this paper, we discuss our Logan event exploration prototype, which is based on regular-expression queries and result histograms; we then discuss the many factors that vary between tools, domains, and datasets — tradeoffs of factors such as session size, cardinality of events, and the presence of event arguments.<\/p>\n","protected":false},"excerpt":{"rendered":"
Event processing, analysis, and visualization are increasingly important, and common, problems as telemetry and log recording become ubiquitous. We are still learning about the space of ways to both query and see the results of sequential queries against event-logged data. In this paper, we discuss our Logan event exploration prototype, which is based on regular-expression […]<\/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":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13563,13554],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-318926","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-data-platform-analytics","msr-research-area-human-computer-interaction","msr-locale-en_us"],"msr_publishername":"","msr_edition":"http:\/\/eventevent.github.io\/","msr_affiliation":"","msr_published_date":"2016-10-24","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-TR-2016-1016","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"http:\/\/eventevent.github.io\/","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":"318929","msr_publicationurl":"http:\/\/eventevent.github.io","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"event_2016_paper_17","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/11\/EVENT_2016_paper_17.pdf","id":318929,"label_id":0},{"type":"url","title":"http:\/\/eventevent.github.io","viewUrl":false,"id":false,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":0,"url":"http:\/\/eventevent.github.io"}],"msr-author-ordering":[{"type":"user_nicename","value":"Danyel Fisher","user_id":31539,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Danyel Fisher"},{"type":"user_nicename","value":"Steven Drucker","user_id":33564,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Steven Drucker"},{"type":"user_nicename","value":"Mary Czerwinski","user_id":32824,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Mary Czerwinski"},{"type":"user_nicename","value":"Rob DeLine","user_id":33370,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Rob DeLine"},{"type":"user_nicename","value":"Kael Rowan","user_id":32466,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Kael Rowan"}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[144794,371909,550641,578422],"msr_project":[171509],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":171509,"post_title":"Logan: Logfile Analysis","post_name":"logan-logfile-analysis","post_type":"msr-project","post_date":"2015-10-12 16:47:52","post_modified":"2019-08-19 18:48:13","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/logan-logfile-analysis\/","post_excerpt":"Understanding Techniques and Tools for More Effective Telemetry and Log Data Analysis. Increasingly, business processes require data-driven real-time feedback based on large quantities of log data and customer telemetry from multiple sources. The Logan Project takes a broad approach to understanding the specific needs of consumers of telemetry and log data, focusing on giving them better support for extracting the data they need, cleaning it, and creating queries against it. 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