Inferring User Actions from Provenance Logs

Progger, a kernel-spaced cloud data provenance logger which provides fine-grained data activity records, was recently developed to empower cloud stakeholders to trace data life cycles within and across clouds. Progger logs have the potential to allow analysts to infer user actions and create a data-centric behaviour history in a cloud computing environment. However, the Progger logs are complex and noisy and therefore, currently this potential can not be met. This paper proposes a statistical approach to efficiently infer the user actions from the Progger logs. Inferring logs which capture activities at kernel-level granularity is not a straightforward endeavour. This paper overcomes this challenge through an approach which shows a high level of accuracy. The key aspects of this approach are identifying the data preprocessing steps and attribute selection. We then use four standard classification models and identify the model which provides the most accurate inference on user actions. To our best knowledge, this is the first work of its kind. We also discuss a number of possible extensions to this work. Possible future applications include the ability to predict an anomalous security activity before it occurs

A provenance task abstraction framework

Visual analytics tools integrate provenance recording to externalize analytic processes or user insights. Provenance can be captured on varying levels of detail, and in turn activities can be characterized from different granularities. However, current approaches do not support inferring activities that can only be characterized across multiple levels of provenance. We propose a task abstraction framework that consists of a three stage approach, composed of (1) initializing a provenance task hierarchy, (2) parsing the provenance hierarchy by using an abstraction mapping mechanism, and (3) leveraging the task hierarchy in an analytical tool. Furthermore, we identify implications to accommodate iterative refinement, context, variability, and uncertainty during all stages of the framework. A use case describes exemplifies our abstraction framework, demonstrating how context can influence the provenance hierarchy to support analysis. The paper concludes with an agenda, raising and discussing challenges that need to be considered for successfully implementing such a framework

A provenance task abstraction framework

Visual analytics tools integrate provenance recording to externalize analytic processes or user insights. Provenance can be captured on varying levels of detail, and in turn activities can be characterized from different granularities. However, current approaches do not support inferring activities that can only be characterized across multiple levels of provenance. We propose a task abstraction framework that consists of a three stage approach, composed of (1) initializing a provenance task hierarchy, (2) parsing the provenance hierarchy by using an abstraction mapping mechanism, and (3) leveraging the task hierarchy in an analytical tool. Furthermore, we identify implications to accommodate iterative refinement, context, variability, and uncertainty during all stages of the framework. A use case describes exemplifies our abstraction framework, demonstrating how context can influence the provenance hierarchy to support analysis. The paper concludes with an agenda, raising and discussing challenges that need to be considered for successfully implementing such a framework

Analytic provenance for sensemaking: A research agenda

Sensemaking is a process of finding meaning from information that often involves activities such as information foraging and hypothesis generation. It can be valuable to maintain a history of the data and reasoning involved. This history, commonly known as provenance information, can be a resource for 'reflection-in-action'' during analysis, supporting collaboration between analysts, and can help trace data quality and uncertainty through the analysis process. Currently, there is limited work on utilizing analytic provenance, which captures the interactive data exploration and human reasoning process, to support sensemaking. This article presents and extends the research challenges discussed in a IEEE VIS 2014 workshop on this topic to provide an agenda for sensemaking analytic provenance

A novel approach to task abstraction to make better sense of provenance data

Working Group Report in 'Provenance and Logging for Sense Making' report from Dagstuhl Seminar 18462: Provenance and Logging for Sense Making, Dagstuhl Reports, Volume 8, Issue 1

Provenance and logging for sense making

Sense making is one of the biggest challenges in data analysis faced by both the industry and the research community. It involves understanding the data and uncovering its model, generating a hypothesis, selecting analysis methods, creating novel solutions, designing evaluation, and also critical thinking and learning wherever needed. The research and development for such sense making tasks lags far behind the fast-changing user needs, such as those that emerged recently as the result of so-called “Big Data”. As a result, sense making is often performed manually and the limited human cognition capability becomes the bottleneck of sense making in data analysis and decision making. One of the recent advances in sense making research is the capture, visualization, and analysis of provenance information. Provenance is the history and context of sense making, including the data/analysis used and the users’ critical thinking process. It has been shown that provenance can effectively support many sense making tasks. For instance, provenance can provide an overview of what has been examined and reveal gaps like unexplored information or solution possibilities. Besides, provenance can support collaborative sense making and communication by sharing the rich context of the sense making process. Besides data analysis and decision making, provenance has been studied in many other fields, sometimes under different names, for different types of sense making. For example, the Human-Computer Interaction community relies on the analysis of logging to understand user behaviors and intentions; the WWW and database community has been working on data lineage to understand uncertainty and trustworthiness; and finally, reproducible science heavily relies on provenance to improve the reliability and efficiency of scientific research. This Dagstuhl Seminar brought together researchers from the diverse fields that relate to provenance and sense making to foster cross-community collaboration. Shared challenges were identified and progress has been made towards developing novel solutions

A novel approach to task abstraction to make better sense of provenance data

Working Group Report in 'Provenance and Logging for Sense Making' report from Dagstuhl Seminar 18462: Provenance and Logging for Sense Making, Dagstuhl Reports, Volume 8, Issue 1

Analytic provenance for sensemaking: a research agenda

Sensemaking is a process of find meaning from information, and often involves activities such as information foraging and hypothesis generation. It can be valuable to maintain a history of the data and reasoning involved, commonly known as provenance information. Provenance information can be a resource for “reflection-in-action” during analysis, supporting collaboration between analysts, and help trace data quality and uncertainty through analysis process. Currently, there is limited work of utilizing analytic provenance, which captures the interactive data exploration and human reasoning process, to support sensemaking. In this article, we present and extend the research challenges discussed in a IEEE VIS 2014 workshop in order to provide an agenda for sensemaking analytic provenance

Inferring Intent from Interaction with Visualization

Today\u27s state-of-the-art analysis tools combine the human visual system and domain knowledge, with the machine\u27s computational power. The human performs the reasoning, deduction, hypothesis generation, and judgment. The entire burden of learning from the data usually rests squarely on the human user\u27s shoulders. This model, while successful in simple scenarios, is neither scalable nor generalizable. In this thesis, we propose a system that integrates advancements from artificial intelligence within a visualization system to detect the user\u27s goals. At a high level, we use hidden unobservable states to represent goals/intentions of users. We automatically infer these goals from passive observations of the user\u27s actions (e.g., mouse clicks), thereby allowing accurate predictions of future clicks. We evaluate this technique with a crime map and demonstrate that, depending on the type of task, users\u27 clicks appear in our prediction set 79\% -- 97\% of the time. Further analysis shows that we can achieve high prediction accuracy after only a short period (typically after three clicks). Altogether, we show that passive observations of interaction data can reveal valuable information about users\u27 high-level goals, laying the foundation for next-generation visual analytics systems that can automatically learn users\u27 intentions and support the analysis process proactively