We consider spatio-temporal databases supporting spatial objects with continuously changing position and extent, termed
moving objects databases
. We formally define a data model for such databases that includes complex evolving spatial structures such as line networks or multi-component regions with holes. The data model is given as a collection of data types and operations which can be plugged as attribute types into any DBMS data model (e.g. relational, or object-oriented) to obtain a complete model and query language. A particular novel concept is the
sliced representation
which represents a temporal development as a set of
units
, where unit types for spatial and other data types represent certain “simple” functions of time. We also show how the model can be mapped into concrete physical data structures in a DBMS environment.
Abstract. Let G = (V, E) be a 2-edge connected, undirected and nonnegatively weighted graph, and let S(r ) be a single source shortest paths tree (SPT) of G rooted at r ∈ V . Whenever an edge e in S(r ) fails, we are interested in reconnecting the nodes now disconnected from the root by means of a single edge e crossing the cut created by the removal of e. Such an edge e is named a swap edge for e. Let S e/e (r ) be the swap tree (no longer an SPT, in general) obtained by swapping e with e , and let S e be the set of all possible swap trees with respect to e. Let F be a function defined over S e that expresses some feature of a swap tree, such as the average length of a path from the root r to all the nodes below edge e, or the maximum length, or one of many others. A best swap edge for e with respect to F is a swap edge f such that F(S e/ f (r )) is minimum.In this paper we present efficient algorithms for the problem of finding a best swap edge, for each edge e of S(r ), with respect to several objectives. Our work is motivated by a scenario in which individual connections in a communication network suffer transient failures. As a consequence of an edge failure, the shortest paths to all the nodes below the failed edge might completely change, and it might be desirable to avoid an expensive switch to a new SPT, because the failure is only temporary. As an aside, what we get is not even far from a new SPT: our analysis shows that the trees obtained from the swapping have features very similar to those of the corresponding SPTs rebuilt from scratch.
Many advanced countries are recognizing more and more the importance of teaching computing, in some cases even as early as in primary school. "Computational thinking" is the term often used to denote the conceptual core of computer science or "the way a computer scientist thinks", as Wing put it. Such term -given also the lack of a widely accepted definition -has become a "buzzword" meaning different things to different people. We investigated the Italian primary school teachers' conceptions about computational thinking by analyzing the results of a survey (N=972) conducted in the context of "Programma il Futuro" project. Teachers have been asked to provide a definition of computational thinking and to answer three additional related closed-ended questions. The analysis shows that, while almost half of teachers (43.4%) have included in their definitions some fundamental elements of computational thinking, very few (10.8%) have been able to provide an acceptably complete definition. On a more positive note, the majority is aware that computational thinking is not characterized by coding or by the use of information technology.
In the past, the problem of finding a most vital node of a given shortest path has been studied because of its implications in network management, where it is important to know in advance which component failure will affect network efficiency the most. In this paper, we show that this problem can be solved in O(m + n log n) time and O(m) space, where m and n denote the number of edges and the number of nodes in G.
Considering the expression "computational thinking" as an entry point to understand why the fundamental contribution of computing to science is the shift from solving problems to having problems solved.
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