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Skyline path queries (SPQs) extend skyline queries to multi-dimensional networks, such as multi-cost road networks (MCRNs). Such queries return a set of non-dominated paths between two given network nodes. Despite the existence of extensive works on evaluating different SPQ variants, SPQ evaluation is still very inefficient due to the nonexistence of efficient index structures to support such queries. Existing index building approaches for supporting shortest-path query execution, when directly extended to support SPQs, use an unreasonable amount of space and time to build, making them impractical for processing large graphs. In this paper, we propose a novel index structure,backbone index, and a corresponding index construction method that condenses an initial MCRN to multiple smaller summarized graphs with different granularity. We present efficient approaches to find approximate solutions to SPQs by utilizing the backbone index structure. Furthermore, considering making good use of historical query and query results, we propose two models,SkylinePathGraphNeuralNetwork (SP-GNN) andTransfer SP-GNN (TSP-GNN), to support effective SPQ processing. Our extensive experiments on real-world large road networks show that the backbone index can support finding meaningful approximate SPQ solutions efficiently. The backbone index can be constructed in a reasonable time, which dramatically outperforms the construction of other types of indexes for road networks. As far as we know, this is the first compact index structure that can support efficient approximate SPQ evaluation on large MCRNs. The results on the SP-GNN and TSP-GNN models also show that both models can help get approximate SPQ answers efficiently.
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A multi‐dimensional index of evaluating systems thinking skills from textual data
Abstract Systems thinking (ST) includes a set of critical skills and approaches for addressing today's complex societal problems. Therefore, it has been introduced into the curricula of many educational programmes around the world. Despite all the attention to ST, there is less consensus when it comes to evaluating and assessing ST skills. Particularly, a quantitative assessment approach that captures ST's multi‐dimensionality is crucial when evaluating the degree to which one has learned and utilizes ST. This paper proposes a systematic approach to create such a multi‐dimensional Index of ST from textual data. Initially, we provide an overview of the theoretical background as it pertains to different measurement approaches of ST skills. Then we provide a conceptual framework based on ST skill measures and transform this framework into a quantifiable model. Finally, using student data, we provide an illustration of an integrated index of ST skills. We compute this index by using a mixed methods approach, including robust principal component analysis, data envelopment analysis and two‐staged bootstrapping approach. The results show that (i) our model serves as a systematic multi‐dimensional ST approach by including multiple measures of ST skills and (ii) international students and self‐reported math skills are found as significant predictors of one's level of ST in the graduate student dataset (N = 30), however no significant factors are found in the first‐year engineering student dataset (N = 144).
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- Award ID(s):
- 1824594
- PAR ID:
- 10560789
- Publisher / Repository:
- International Federation for Systems Research
- Date Published:
- Journal Name:
- Systems Research and Behavioral Science
- ISSN:
- 1092-7026
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1002/sres.3033
