CS Events Monthly View
Searching Heterogeneous Personal Data
Friday, September 06, 2019, 01:00pm
Personal data is now pervasive, as digital devices are capturing every part of our lives. Users are constantly collecting and saving more data, either actively in files, emails, social media interactions, etc., or passively by GPS tracking of mobile devices, or records of financial transactions. Unlike traditional information seeking, which focuses on discovering new information, search on personal data is usually focused on retrieving information that users know exists in their own dataset, even though most of the time they do not have a perfect recollection of where it is stored. Attempting to retrieve and cross-reference personal information leads to a tedious process of individually accessing all the relevant sources of data and manually linking their information. In this scenario, traditional searches are often inefficient, making it critical for search tools to be capable of accessing heterogeneous and decentralized data in a flexible and accurate way by taking into consideration the additional knowledge the user is likely to have about the target information.
In this dissertation, we introduce a set of techniques that allow users to easily access their own data. We start by presenting a unified and intuitive multidimensional data model following a combination of dimensions that naturally summarize various aspects of the data collection: who, when, where, what, why, how. We then proceed by designing frequency-based scoring models that leverage the correlation between users (who), time (when), location (where), data topics (what), and provenance (how) to improve search over personal data. Since the scoring model proposed needs to generalize well over user-specific datasets, we extend the static scoring function by adopting a learning-to-rank approach using the state of the art LambdaMART algorithm. Due to the lack of pre-existing personal training data, a combination of known-item query generation techniques and an unsupervised ranking model (field-based BM25) is used to build our own training sets.
To validate the data and scoring models, we implemented tools for data extraction, classification, entity recognition, and topic modeling. A thorough qualitative evaluation performed over a publicly available email collection and a personal digital data trace collection from a real user show that our approach significantly improves search accuracy when compared with traditional personal search tools such as Apple's Spotlight and Apache Solr, and techniques like TF-IDF, BM25, and field-based BM25.
Speaker: Daniela Vianna
Location : CoRE A (301)
Prof. Amelie Marian (Chair), Prof. Thu Nguyen, Prof. Yongfeng Zhang, and Dr. Divesh Srivastava
Event Type: PhD Defense
Dept. of Computer Science