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The volume, variety, and velocity of different data, e.g., simulation data, observation data, and social media data, are growing ever faster, posing grand challenges for data discovery. An increasing trend in data discovery is to mine hidden relationships among users and metadata from the web usage logs to support the data discovery process. Web usage log mining is the process of reconstructing sessions from raw logs and finding interesting patterns or implicit linkages. The mining results play an important role in improving quality of search-related components, e.g., ranking, query suggestion, and recommendation. While researches were done in the data discovery domain, collecting and analyzing logs efficiently remains a challenge because (1) the volume of web usage logs continues to grow as long as users access the data; (2) the dynamic volume of logs requires on-demand computing resources for mining tasks; (3) the mining process is compute-intensive and time-intensive. To speed up the mining process, we propose a cloud-based log-mining framework using Apache Spark and Elasticsearch. In addition, a data partition paradigm, logPartitioner, is designed to solve the data imbalance problem in data parallelism. As a proof of concept, oceanographic data search and access logs are chosen to validate performance of the proposed parallel log-mining framework.
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Pairing Users in Social Media via Processing Meta-data from Conversational Files
Massive amounts of data today are being generated from users engaging on social media. Despite knowing that whatever they post on social media can be viewed, downloaded and analyzed by unauthorized entities, a large number of people are still willing to compromise their privacy today. On the other hand though, this trend may change. Improved awareness on protecting content on social media, coupled with governments creating and enforcing data protection laws, mean that in the near future, users may become increasingly protective of what they share. Furthermore, new laws could limit what data social media companies can use without explicit consent from users. In this paper, we present and address a relatively new problem in privacy-preserved mining of social media logs. Specifically, the problem here is the feasibility of deriving the topology of network communications (i.e., match senders and receivers in a social network), but with only meta-data of conversational files that are shared by users, after anonymizing all identities and content. More explicitly, if users are willing to share only (a) whether a message was sent or received, (b) the temporal ordering of messages and (c) the length of each message (after anonymizing everything else, including usernames from their social media logs), how can the underlying topology of sender-receiver patterns be generated. To address this problem, we present a Dynamic Time Warping based solution that models the meta-data as a time series sequence. We present a formal algorithm and interesting results in multiple scenarios wherein users may or may not delete content arbitrarily before sharing. Our performance results are very favorable when applied in the context of Twitter. Towards the end of the paper, we also present interesting practical applications of our problem and solutions. To the best of our knowledge, the problem we address and the solution we propose are unique, and could provide important future perspectives on learning from privacy-preserving mining of social media logs.
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- Award ID(s):
- 1718071
- PAR ID:
- 10179453
- Date Published:
- Journal Name:
- Big-Data Analytics (BDA)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-37188-3_7
