{"title":"Event Mining and Pattern Discovery","authors":"","doi":"10.1145/3462257.3462261","DOIUrl":"https://doi.org/10.1145/3462257.3462261","url":null,"abstract":"time. Increasingly autonomous systems are being developed to make these sensi tive decisions in critical situations. Big data has ushered in a clear departure from the earlier use of data streams, which was primarily for understanding consumer behavior or for helping people to purchase stocks. An implicit assumption in data systems in the last century was that objects are primary and events are just the properties of an object. In contrast, this century’s data systems place events at the same level as objects. What ancient philosophers [Casati and Varzi 2015] believed about the world being represented by objects and events is finally coming to computers and cyberspaces. With the increasing avail ability of sensor data streams that represent diverse attributes for objects and locations, events are becoming as important as objects. As discussed in Chapter 2, an event has multiple properties, usually recognized at different levels of granularity, and represented with an event model. Depending on the complexity of an application, the event model might either contain all facets (i.e., informational, structural, experiential, spatial, temporal, and causal), or only a few of them. In its simplest form, however, an event model must contain infor mational and temporal facets: the event type or name is needed as a humanand machine-understandable label, and a timestamp is needed because events occur at a certain moment in time or span an interval. Event streams have two main dimensions: (1) temporal sequence, where data are indexed by time, and (2) infor mational segment, where data are encapsulated in events’ properties (such as type, name, location, participants, etc.). As shown in Figure 2.3, event streams contain a Event Mining and Pattern Discovery","PeriodicalId":208013,"journal":{"name":"Event Mining for Explanatory Modeling","volume":"245 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134387947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Conclusion and Future Direction","authors":"Xilin Liu, J. Spiegel","doi":"10.1145/3462257.3462265","DOIUrl":"https://doi.org/10.1145/3462257.3462265","url":null,"abstract":"This book has presented the analysis and design of BMI systems. To the best of our knowledge, this is the first work dedicated to studying bidirectional closed-loop BMI systems. The main motivation of this work is the fact that many significantly meaningful neuroscience experiments, especially in freely behaving animals, cannot be conducted without custom designed bidirectional closed-loop BMIs. With the close collaboration between neuroscientists and engineers, this work was able to identify and address several important and practical issues in BMI systems’ design. The developed system has been successfully used in several animal experiments, resulting in significant new observations.","PeriodicalId":208013,"journal":{"name":"Event Mining for Explanatory Modeling","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128732279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Event Mining for Explanatory Modeling","authors":"Laleh Jalali, R. Jain","doi":"10.1145/3462257","DOIUrl":"https://doi.org/10.1145/3462257","url":null,"abstract":"","PeriodicalId":208013,"journal":{"name":"Event Mining for Explanatory Modeling","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127586413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Think Events: From Signals to Events","authors":"","doi":"10.1145/3462257.3462260","DOIUrl":"https://doi.org/10.1145/3462257.3462260","url":null,"abstract":"With more challenging problems arising in recent times, it became essential for data management research to start considering dynamic situations, in particular data streams [Babcock et al. 2002] and the events happening within them. Luckham and Frasca [1998] championed the concept of complex event processing in data streams, an idea that was adopted by many researchers and remains popular in traditional applications that process a few well-structured data streams for making real-time decisions. More challenging problems have pushed the concept of events to something that resembles more of what we see in foundational sciences such as in philosophy and linguistics. The concept of events and applications that consider events as important entities is now an emerging trend. Westermann and Jain [2007] proposed a six-facet model to represent event structure, attributes, and causality. Xie et al. [2008] proposed a 5W1H (What, Who, Where, When, Why, How) repre sentation to capture event attributes. In databases, Gatziu and Dittrich [1994] and Gehani et al. [1992] proposed models based on different event attributes. In most of these models (save for Westermann and Jain [2007]), causality and structure were not captured. The term event has been used in two distinct contexts in the computing litera ture: physical world occurrences and the representations of those occurrences in a computer system. In different computer science domains, event-based analysis is about capturing, processing, and managing low-level events such as publish/sub scribe systems and middleware solutions [Oberle 2006], complex event processing [Ericsson and Berndtsson 2007], event stream processing [Cetintemel 2003], and Think Events: From Signals to Events","PeriodicalId":208013,"journal":{"name":"Event Mining for Explanatory Modeling","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127913161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"EventMiner Framework","authors":"","doi":"10.1145/3462257.3462264","DOIUrl":"https://doi.org/10.1145/3462257.3462264","url":null,"abstract":"pattern formulation and pattern mining language. The language is composed of a well-defined set of operators that facilitate pattern analysis. Hypothesis forma tion is achieved by data-driven operators that bring hidden interesting patterns to the surface. Hypothesis testing is accomplished by hypothesis-driven operators that facilitate knowledge formulation and pattern query. Data-driven operators are used to generate a basic model and derive a prelim inary insight. Then, an analyst can seed a hypothesis and grow it step by step using hypothesis-driven operators. A good hypothesis is one that is not necessarily correct but one that opens new paths of investigation. This path cannot be fully perceived in advance in complex modeling tasks. So the analyst must be provided with appropriate operators to carry out new experiments based on the original hypothesis. EventMiner Framework","PeriodicalId":208013,"journal":{"name":"Event Mining for Explanatory Modeling","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125655725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Event Mining Applications","authors":"","doi":"10.1145/3462257.3462263","DOIUrl":"https://doi.org/10.1145/3462257.3462263","url":null,"abstract":"can be defined as a system, from a single component in a jet engine manufacturing line to ubiquitous computing. As the system operates, events can be extracted and subsequently mined through a variety of methods. Unwanted events can be filtered, anomaly events can be detected, complex event processing (CEP) can be applied to aggregate and monitor the occurrence of correspondence between multiple events, significant patterns of multiple events can be extracted, and so on. To understand complex systems and deal with complex patterns, logging sys tems’ events with only an ID, name, and timestamp is not enough. As mentioned in Chapter 2, events need to be stored as a complex object with all their properties and relationships rather than a relational tuple. Event mining algorithms not only need to support symbolic representation of events but also complex relationships such as causality, where one event is the root cause of another. In this chapter, we discuss how event mining can be applied in different domains and overview general requirements and high-level workflow in each domain. Event Mining Applications","PeriodicalId":208013,"journal":{"name":"Event Mining for Explanatory Modeling","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116742585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Design Principles of Event Mining Systems","authors":"","doi":"10.1145/3462257.3462262","DOIUrl":"https://doi.org/10.1145/3462257.3462262","url":null,"abstract":"to assist researchers, analysts, and decision makers in extracting knowledge from such a variety of data. As the speed and scale of this data generation will increase even further in the future, we require new frameworks that support highperformance computing, processing techniques that fuse heterogeneous data and uncover hidden patterns and unknown correlations, scalable software tools, and useful visualizations that help analysts and decision makers understand the results better. Modeling complex, mysterious, and at least partly unknowable systems involves many complicated decisions such as determining a model selection strategy, defin ing a model structure, defining a criteria for model goodness, selecting data and the transformations applied to the data, tuning learning parameters, and so on. Most of these decisions involve a reliance on theoretical or empirical results, that is, expert domain knowledge, and cannot be learned by a system itself solely from available input data. Moreover, many spurious associations might arise from learned models, resulting in false scientific discoveries and false statistical infer ences [Calude and Longo 2017]. A promising approach for modeling complex phe nomenon is to adopt a human-in-the-loop approach in the data processing step. This integrates high-level expert knowledge into the modeling process by acquiring an expert’s relevance judgments regarding a set of initial retrieval results. Despite the apparent benefits of such a perspective, frameworks that facilitate a seam less interaction between a domain expert and a traditional knowledge discovery process are not well studied. Figure 4.1 shows the human-in-the-loop in a modeling process rooted in event mining. Design Principles of Event Mining Systems","PeriodicalId":208013,"journal":{"name":"Event Mining for Explanatory Modeling","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127708484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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