Time-Series-Based Queries on Stable Transportation Networks Equipped with Sensors

In this paper, we propose a formalism to query transportation networks that are equipped with sensors that produce time-series data. The core of the proposed query mechanism is a logic-based language that is capable to return time, value, and time-series outputs, as well as Boolean queries. We can also use the language for node selection and path selection. Furthermore, we propose an implementation of this language in a graph database system and evaluate its working on a fragment of the Flemish river system that is equipped with sensors that measure the water height at regular moments in time.

Refining Boolean queries to identify relevant studies for systematic review updates

Objective Systematic reviews are important in health care but are expensive to produce and maintain. The authors explore the use of automated transformations of Boolean queries to improve the identification of relevant studies for updates to systematic reviews. Materials and Methods A set of query transformations, including operator substitution, query expansion, and query reduction, were used to iteratively modify the Boolean query used for the original systematic review. The most effective transformation at each stage is identified using information about the studies included and excluded from the original review. A dataset consisting of 22 systematic reviews was used for evaluation. Updated queries were evaluated using the included and excluded studies from the updated version of the review. Recall and precision were used as evaluation measures. Results The updated queries were more effective than the ones used for the original review, in terms of both precision and recall. The overall number of documents retrieved was reduced by more than half, while the number of relevant documents found increased by 10.3%. Conclusions Identification of relevant studies for updates to systematic reviews can be carried out more effectively by using information about the included and excluded studies from the original review to produce improved Boolean queries. These updated queries reduce the overall number of documents retrieved while also increasing the number of relevant documents identified, thereby representing a considerable reduction in effort required by systematic reviewers.

Private Set Operations Over Encrypted Cloud Dataset and Applications

We introduce the notion of private set operations (PSO) as a symmetric-key primitive in the cloud scenario, where a client securely outsources his dataset to a cloud service provider and later privately issues queries in the form of common set operations. We define a syntax and security notion for PSO and propose a general construction that satisfies it. There are two main ingredients to our PSO scheme: an adjustable join (Adjoin) scheme (MIT-CSAIL-TR-2012-006 (2012) Cryptographic treatment of CryptDB’s adjustable join. http://people.csail.mit.edu/nickolai/papers/popa-join-tr.pdf) and a tuple set (TSet) scheme (Cash, D., Jarecki, S., Jutla, C. S., Krawczyk, H., Rosu, M.-C., and Steiner, M. (2013) Highly-Scalable Searchable Symmetric Encryption With Support for Boolean Queries. 33rd Annual Cryptology Conf., Santa Barbara, CA, August 18–22, pp. 353–373. Springer, Berlin, Heidelberg). We also propose an Adjoin construction that is substantially more efficient (in computation and storage) than the previous ones (Mironov, I., Segev, G., and Shahaf, I. (2017) Strengthening the Security of Encrypted Databases: Non-Transitive Joins. 15th Int. Conf., TCC 2017, Baltimore, MD, USA, November 12–15, pp. 631–661. Springer, Cham) due to the hardness assumption that we rely on, while retaining the same security notion. The proposed PSO scheme can be used to perform join queries on encrypted databases without revealing the duplicate patterns in the unqueried columns, which is inherent to an Adjoin scheme. In addition, we also show that the PSO scheme can be used to perform Boolean search queries on a collection of encrypted documents. We also provide standard security proofs for our constructions and present detailed efficiency evaluation and compare them with well-known previous ones.

Evaluation of patient-level retrieval from electronic health record data for a cohort discovery task

Objective Growing numbers of academic medical centers offer patient cohort discovery tools to their researchers, yet the performance of systems for this use case is not well understood. The objective of this research was to assess patient-level information retrieval methods using electronic health records for different types of cohort definition retrieval. Materials and Methods We developed a test collection consisting of about 100 000 patient records and 56 test topics that characterized patient cohort requests for various clinical studies. Automated information retrieval tasks using word-based approaches were performed, varying 4 different parameters for a total of 48 permutations, with performance measured using B-Pref. We subsequently created structured Boolean queries for the 56 topics for performance comparisons. In addition, we performed a more detailed analysis of 10 topics. Results The best-performing word-based automated query parameter settings achieved a mean B-Pref of 0.167 across all 56 topics. The way a topic was structured (topic representation) had the largest impact on performance. Performance not only varied widely across topics, but there was also a large variance in sensitivity to parameter settings across the topics. Structured queries generally performed better than automated queries on measures of recall and precision but were still not able to recall all relevant patients found by the automated queries. Conclusion While word-based automated methods of cohort retrieval offer an attractive solution to the labor-intensive nature of this task currently used at many medical centers, we generally found suboptimal performance in those approaches, with better performance obtained from structured Boolean queries. Future work will focus on using the test collection to develop and evaluate new approaches to query structure, weighting algorithms, and application of semantic methods.

A restricted second-order logic for non-deterministic poly-logarithmic time

We introduce a restricted second-order logic $\textrm{SO}^{\textit{plog}}$ for finite structures where second-order quantification ranges over relations of size at most poly-logarithmic in the size of the structure. We demonstrate the relevance of this logic and complexity class by several problems in database theory. We then prove a Fagin’s style theorem showing that the Boolean queries which can be expressed in the existential fragment of $\textrm{SO}^{\textit{plog}}$ correspond exactly to the class of decision problems that can be computed by a non-deterministic Turing machine with random access to the input in time $O((\log n)^k)$ for some $k \ge 0$, i.e. to the class of problems computable in non-deterministic poly-logarithmic time. It should be noted that unlike Fagin’s theorem which proves that the existential fragment of second-order logic captures NP over arbitrary finite structures, our result only holds over ordered finite structures, since $\textrm{SO}^{\textit{plog}}$ is too weak as to define a total order of the domain. Nevertheless, $\textrm{SO}^{\textit{plog}}$ provides natural levels of expressibility within poly-logarithmic space in a way which is closely related to how second-order logic provides natural levels of expressibility within polynomial space. Indeed, we show an exact correspondence between the quantifier prefix classes of $\textrm{SO}^{\textit{plog}}$ and the levels of the non-deterministic poly-logarithmic time hierarchy, analogous to the correspondence between the quantifier prefix classes of second-order logic and the polynomial-time hierarchy. Our work closely relates to the constant depth quasipolynomial size AND/OR circuits and corresponding restricted second-order logic defined by David A. Mix Barrington in 1992. We explore this relationship in detail.

On the Expressivity of ASK Queries in SPARQL

As a major query type in SPARQL, ASK queries are boolean queries and have found applications in several domains such as semantic SPARQL optimization. This paper is a first systematic study of the relative expressive power of various fragments of ASK queries in SPARQL. Among many new results, a surprising one is that the operator UNION is redundant for ASK queries. The results in this paper as a whole paint a rich picture for the expressivity of fragments of ASK queries with the four basic operators of SPARQL 1.0 possibly together with a negation. The work in this paper provides a guideline for future SPARQL query optimization and implementation.