SIGMOD '19 Proceedings of the 2019 International Conference on Management of Data / A Scalable Index for Top-k Subtree Similarity Queries

Given a query tree Q, the top-k subtree similarity query retrieves the k subtrees in a large document tree T that are closest to Q in terms of tree edit distance. The classical solution scans the entire document, which is slow. The state-of-the-art approach precomputes an index to reduce the query time. However, the index is large (quadratic in the document size), building the index is expensive, updates are not supported, and data-specific tuning is required. We present a scalable solution for the top-k subtree similarity problem that does not assume specific data types, nor does it require any tuning. The key idea is to process promising subtrees first. A subtree is promising if it shares many labels with the query. We develop a new technique based on inverted lists that efficiently retrieves subtrees in the required order and supports incremental updates of the document. To achieve linear space, we avoid full list materialization but build relevant parts of a list on the fly. In an extensive empirical evaluation on synthetic and real-world data, our technique consistently outperforms the state-of-the-art index w.r.t. memory usage, indexing time, and the number of candidates that must be verified. In terms of query time, we clearly outperform the state of the art and achieve runtime improvements of up to four orders of magnitude.(VLID)441194

Three-dimensional tumor volume and serum alpha-fetoprotein are predictors of hepatocellular carcinoma recurrence after liver transplantation: refined selection criteria

Total tumor volume (TTV), as a better predictor of hepatocellular carcinoma (HCC) recurrence after liver transplant, has been explored by our center. Some tumors are not typically spherical but rather ellipsoid or spheroid, and calculating their TTV based on one dimension only may overestimate their volume and exclude them from candidacy for transplantation. Our aim was to study the actual tumor volume (ATV) calculated using the ellipsoid formula and assess its impact on recurrence. HCC patients transplanted between 1990 and 2010 at University of Alberta Hospital were analyzed. Tumor volumes were calculated using both formulas: [(4/3) πr(3)] (r = max. radius) and [(4/3) πabc] (a, b, c = the 3 radiuses). A total of 115 patients were included with a mean follow-up of 4.99 ± 4.23 yr. Five-yr recurrence-free survival was 79.8%. Univariate analysis for predictors of recurrence included: maximum tumor diameter, ATV, TTV, and alpha-fetoprotein (AFP) ≥ 400 ng/mL. Multivariate analysis showed that ATV and AFP ≥ 400 ng/mL were the only predictors of recurrence. Combining both variables provides better predication of recurrence with accuracy that exceeds 80%. Three-dimensional calculation of tumor volume is of critical importance for the group of patients with ellipsoid tumors where volumes are overestimated with the spherical formula and could lead to inappropriate exclusion from transplant

Identification of Platform Candidates Through Production System Classification Coding

Part 5: Variety and Complexity Management in the Era of Industry 4.0International audienceChangeable and reconfigurable manufacturing appears as a natural response to a need for improved variety management. Such manufacturing systems are complicated to develop, and it can be advantageous to base or build these systems on product and production platforms. Development of platforms is, however, not a trivial task. Currently, identification and selection of candidates for inclusion in a platform is typically subjective relying on experts and tacit knowledge. The objectivity of this process can be strengthened by collecting data on existing production systems in a company and comparing these systems to each other. To do so, a coherent, consistent and preferably digital representation of multiple production systems is needed. In this research, a production system classification coding (PSCC) scheme is employed to classify and structure data for a number of existing production systems, spanning multiple departments and product families. Candidates for a production platform covering the included production systems are identified based on ranking certain platform drivers, processes and enablers