Marios Iakovidis | Searcharxiv

arXiv2026

Semantics and Multi-Query Optimization Algorithms for the Analyze Operator

In their hunt for highlights, i.e., interesting patterns in the data, data analysts have to issue groups of related queries and manually combine their results. To the extent that the analyst's goals are based on an intention on what to discover (e.g., contrast a query result to peer ones, verify a pattern to a broader range of data in the data space, etc), the integration of intentional query operators in analytical engines can enhance the efficiency of these analytical tasks. In this paper, we introduce, with well-defined semantics, the ANALYZE operator, a novel cube querying intentional operator that provides a 360 view of data. We define the semantics of an ANALYZE query as a tuple of five internal, facilitator cube queries, that (a) report on the specifics of a particular subset of the data space, which is part of the query specification, and to which we refer as the original query, (b) contrast the result with results from peer-subspaces, or sibling queries, and, (c) explore the data space in lower levels of granularity via drill-down queries. We introduce formal query semantics for the operator and we theoretically prove that we can obtain the exact same result by merging the facilitator cube queries into a smaller number of queries. This effectively introduces a multi-query optimization (MQO) strategy for executing an ANALYZE query. We propose three alternative algorithms, (a) a simple execution without optimizations (Min-MQO), (b) a total merging of all the facilitator queries to a single one (Max-MQO), and (c) an intermediate strategy, Mid-MQO, that merges only a subset of the facilitator queries. Our experimentation demonstrates that Mid-MQO achieves consistently strong performance across several contexts, Min-MQO always follows it, and Max-MQO excels for queries where the siblings are sizable and significantly overlap.

cs.DB↗

arXiv2013

Transport properties and electrical device characteristics with the TiMeS computational platform: application in silicon nanowires

Nanoelectronics requires the development of a priori technology evaluation for materials and device design that takes into account quantum physical effects and the explicit chemical nature at the atomic scale. Here, we present a cross-platform quantum transport computation tool. Using first-principles electronic structure, it allows for flexible and efficient calculations of materials transport properties and realistic device simulations to extract current-voltage and transfer characteristics. We apply this computational method to the calculation of the mean free path in silicon nanowires with dopant and surface oxygen impurities. The dependence of transport on basis set is established, with the optimized double zeta polarized basis giving a reasonable compromise between converged results and efficiency. The current-voltage characteristics of ultrascaled (3 nm length) nanowire-based transistors with p-i-p and p-n-p doping profiles are also investigated. It is found that charge self-consistency affects the device characteristics more significantly than the choice of the basis set. These devices yield source-drain tunneling currents in the range of 0.5 nA (p-n-p junction) to 2 nA (p-i-p junction), implying that junctioned transistor designs at these length scales would likely fail to keep carriers out of the channel in the off-state.

cond-mat.mes-hall↗