JSON keyword search searches the current versions of documents in a collection. However, JSON documents change over time due to edits. Some applications, such as data forensics and auditing, need to search past versions of documents and for changes to documents. This paper introduces a system called Temporal JSON Keyword Search (TJKS) for search in a collection of JSON documents that vary over time. TJKS lets users control which temporal slice, or part of the history, can be searched using a temporal search semantics; we support both of the major temporal semantics: sequenced and nonsequenced search. This paper presents the semantics of temporal JSON keyword search, discusses an efficient implementation, and evaluates the implementation. Our extensions are largely orthogonal to specific keyword search techniques, so this research provides a blueprint for extending keyword search to include time and potentially other kinds of metadata.

Estimating the quality of Personal Identifiable Information (PII) creates a corresponding need to model and formalize PII for both the real-world and electronic data, in a way that supports rigorous reasoning relative to real-world constraints, rules from domain experts, and rules about expected data patterns. In this paper, we propose an extended first-order logic language (FOL), called PDFOL (Person Data First-order Logic), that can express these kinds of constraints and rules, as well as relevant person attributes and inter-person relations. We present the salient features of PDFOL, namely temporal predicated based on time intervals, aggregate functions, and tuple-set comparison operators. These features allow PDFOL to model person-centric databases, enabling formal and efficient reason about their quality. We show how PDFOL can express real-world constraints, expert opinions, and aggregate knowledge as closed PDFOL statements, which in turn can then be used to assess the quality of a database. We adapt and extend the traditional aggregate functions in three ways: a) allowing any arbitrary number free variables in function statement, b) adding groupings, and c) defining new aggregate function. We believe that this work will provide a foundation for future methods for reasoning about the quality of PII.

Sequenced and nonsequenced semantics were introduced as different semantics for the evaluation of an operation in a temporal database such as a query or data modification. In this paper we present a new framework that shows both semantics are variants of a general temporal semantics. Our framework uses tuple lineage to track tuples through operations. We present the general semantics and show additional semantics that can be realized. We also show how the semantics can be woven into an operation using annotations.