Data stream processing has recently received increasing attention as a computational paradigm for dealing with massive data sets. Surprisingly, no algorithm with both sublinear space and passes is known for natural graph problems in classical read-only streaming. Motivated by technological factors of modern storage systems, some authors have recently started to investigate the computational power of less restrictive models where writing streams is allowed. In this paper, we show that the use of intermediate temporary streams is powerful enough to provide effective spacepasses tradeoffs for natural graph problems. In particular, for any space restriction of s bits, we show that single-source shortest paths in directed graphs with small positive integer edge weights can be solved in O((n log 3/2 n)/ √ s) passes. The result can be generalized to deal with multiple sources within the same bounds. This is the first known streaming algorithm for shortest paths in directed graphs. For undirected connectivity, we devise an O((n log n)/s) passes algorithm. Both problems require Ω(n/s) passes under the restrictions we consider. We also show that the model where intermediate temporary streams are allowed can be strictly more powerful than classical streaming for some problems, while maintaining all of its hardness for others.

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Data stream processing has recently received increasing attention as a computational paradigm for dealing with massive data sets. While major progress has been achieved for several fundamental data sketching and statistics problems, there are many problems that seem to be hard in a streaming setting, including most classical graph problems. Relevant examples are graph connectivity and shortest paths, for which linear lower bounds on the "space x passes" product are known. Some recent papers have shown that several graph problems can be solved with one or few passes, if the working memory is large enough to contain all the vertices of the graph. A natural question is whether we can reduce the space usage at the price of increasing the number of passes. Surprisingly, no algorithm with both sublinear space and passes is known for natural graph problems in classical streaming models. Motivated by technological factors of modern storage systems, some authors have recently started to investigate the computational power of less restrictive streaming models. In a FOCS'04 paper, Aggarwal et al. have shown that the use of intermediate temporary streams, combined with the ability to reorder them at each pass for free, yields enough power to solve in polylogarithmic space and passes a variety of problems, including graph connectivity. They leave however as an open question whether problems such as shortest paths can be solved efficiently in this more powerful model. In this paper, we show that the "streaming with sorting" model by Aggarwal et al. can yield interesting results even without using sorting at all: by just using intermediate temporary streams, we provide the first effective spacepasses tradeoffs for natural graph problems. In particular, for any space restriction of s bits, we show that single-source shortest paths in directed graphs with small positive integer edge weights can be solved in O((n log(3/2) n)/root 7s) passes. This is the first known streaming algorithm for shortest paths in directed graphs. For undirected connectivity, we devise an O((n log n)/s) passes algorithm. Both problems require Omega(n/s) passes under the restrictions we consider. We also show that the model where intermediate temporary streams are allowed can be strictly more powerful than classical streaming for some problems, while maintaining all of its hardness for others

We introduce and investigate a new notion of resilience in graph spanners. Let S be a spanner of a weighted graph G. Roughly speaking, we say that S is resilient if all its pointto-point distances are resilient to edge failures. Namely, whenever any edge in G fails, then as a consequence of this failure all distances do not degrade in S substantially more than in G (i.e., the relative distance increases in S are very close to those in the underlying graph G). In this paper we show that sparse resilient spanners exist, and that they can be computed efficiently.

The vitality of an arc/node of a graph with respect to the maximum flow between two fixed nodes s and t is defined as the reduction of the maximum flow caused by the removal of that arc/node. In this paper, we address the issue of determining the vitality of arcs and/or nodes for the maximum flow problem. We show how to compute the vitality of all arcs in a general undirected graph by solving only 2(n − 1) max flow instances and, in st‐planar graphs (directed or undirected) we show how to compute the vitality of all arcs and all nodes in O(n) worst‐case time. Moreover, after determining the vitality of arcs and/or nodes, and given a planar embedding of the graph, we can determine the vitality of a “contiguous” set of arcs/nodes in time proportional to the size of the set.

Dataflow languages provide natural support for specifying constraints between objects in dynamic applications, where programs need to react efficiently to changes of their environment. Researchers have long investigated how to take advantage of dataflow constraints by embedding them into procedural languages. Previous mixed imperative/dataflow systems, however, require syntactic extensions or libraries of ad hoc data types for binding the imperative program to the dataflow solver. In this paper we propose a novel approach that smoothly combines the two paradigms without placing undue burden on the programmer.In our framework, programmers can define ordinary statements of the imperative host language that enforce constraints between objects stored in special memory locations designated as "reactive". Differently from previous approaches, reactive objects can be of any legal type in the host language, including primitive data types, pointers, arrays, and structures. Statements defining constraints are automatically re-executed every time their input memory locations change, letting a program behave like a spreadsheet where the values of some variables depend upon the values of other variables. The constraint solving mechanism is handled transparently by altering the semantics of elementary operations of the host language for reading and modifying objects. We provide a formal semantics and describe a concrete embodiment of our technique into C/C++, showing how to implement it efficiently in conventional platforms using off-the-shelf compilers. We discuss common coding idioms and relevant applications to reactive scenarios, including incremental computation, observer design pattern, and data structure repair. The performance of our implementation is compared to ad hoc problem-specific change propagation algorithms, as well as to language-centric apPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. OOPSLA'11, October 22-27, 2011, Portland, Oregon, USA. Copyright c 2011 proaches such as self-adjusting computation and subject/observer communication mechanisms, showing that the proposed approach is efficient in practice.

Due to its quantitative nature, bibliometrics is becoming increasingly popular among policy makers for academic hiring and career promotions. In this article, we quantitatively assess the impact that the granularity level in the classification of scientific areas would entail on research evaluation based on bibliometric indicators. We use as a case study the Italian national habilitation system (ASN), which classifies faculty members according to their academic discipline and relies on journal counts, citations, and h-indices as a basis for promoting tenure track researchers to associate professors and associate to full professors. The assessment checks whether the individual indicators of a researcher are above a certain threshold, e.g., the median over the population of researchers working in the same discipline. Our investigation focuses on two related, rather broad disciplines: computer science and computer engineering. We show that the ASN practice of using the same thresholds for all members of a scientific discipline can favor certain sub-communities that are characterized by higher bibliometric indicators, and disfavor others. We report evidence that up to 30% of Italian faculty members of certain sub-communities would see their indicators drop below the threshold, thus becoming not eligible for promotion, if the ASN were conducted on a more accurate, fine-grained classification. Conversely, in the same scenario, up to 11% of faculty members, in different sub-communities, would see their indicators rise above the threshold, granting them eligibility. Our data set includes 1,685 authors, 89,185 distinct publications, and 262,286 author-publication pairs.

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