Abstract: Tourists become increasingly dependent on mobile city guides to locate tourist services and retrieve information about nearby pointsofinterest (POIs) when visiting unknown destinations. Although several city guides support the provision of personalized tour recommendations to assist tourists visiting the most interesting attractions, existing tour planners only consider walking tours. Herein, we introduce eCOMPASS, a context-aware mobile application which also considers the option of using public transit for moving around. Far beyond than just providing navigational aid, eCOMPASS incorporates multimodality (i.e. time dependency) within its routing logic aiming at deriving nearoptimal sequencing of POIs along recommended tours so as to best utilize time available for sightseeing and minimize waiting time at transit stops. Further advancing the state of the art, eCOMPASS allows users to define arbitrary start/end locations(e.g. the current location of a mobile user) rather than choosing among a fixed set of locations. This paper describes the routing algorithm which comprises the core functionality of eCOMPASS
and discusses the implementation details of the mobile application using the metropolitan area of Berlin (Germany) as case study

Abstract: The Time Dependent Team Orienteering Problem with Time Windows (TDTOPTW) can be used to model several real life problems. Among them, the route planning problem for tourists interested in visiting multiple pointsofinterest (POIs) using public transport. The main objective of this problem is to select POIs that match tourist preferences, while taking into account a multitude of parameters and constraints and respecting the time available for sightseeing in a daily basis. TDTOPTW is NP-hard while almost the whole body of the related literature addresses the non time dependent version of the problem. The only TDTOPTW heuristic proposed so far is based on the assumption of periodic service schedules. Herein, we propose two efficient cluster-based heuristics for the TDTOPTW which yield high quality solutions, take into account time dependency in calculating travel times between POIs and make no assumption on periodic service schedules. The validation scenario for our prototyped algorithms included the metropolitan transit network and real POI sets compiled from Athens (Greece).

Abstract: In this paper we examine the problem of searching for some information item in the nodes of a fully
interconnected computer network, where each node contains information relevant to some topic
as well as links to other network nodes that also contain information, not necessarily related to
locally kept information. These links are used to facilitate the Internet users and mobile software
agents that try to locate specific pieces of information. However, the links do not necessarily point
to nodes containing information ofinterest to the user or relevant to the aims of the mobile agent.
Thus an element of uncertainty is introduced. For example, when an Internet user or some search
agent lands on a particular network node, they see a set of links that point to information that is,
supposedly, relevant to the current search. Therefore, we can assume that a link points to relevant
information with some unknown probability p that, in general, is related to the number of nodes
in the network (intuitively, as the network grows, this probability tends to zero since adding more
nodes to the network renders some extant links less accurate or obsolete). Consequently, since there
is uncertainty as to whether the links contained in a node?s Web page are correct or not, a search
algorithm cannot rely on following the links systematically since it may end up spending too much
time visiting nodes that contain irrelevant information. In this work, we will describe and analyze
a search algorithm that is only allowed to transfer a fixed amount of memory along communication
links as it visits the network nodes. The algorithm is, however, allowed to use one bit of memory at
each node as an ?already visited? flag. In this way the algorithm has its memory distributed to the
network nodes, avoiding overloading the network links as it moves from node to node searching for
the information. We work on fully interconnected networks for simplicity reasons and, moreover,
because according to some recent experimental evidence, such networks can be considered to be a
good approximation of the current structure of the World Wide Web.

Abstract: The promises inherent in users coming together to form data
sharing network communities, bring to the foreground new problems formulated
over such dynamic, ever growing, computing, storage, and networking
infrastructures. A key open challenge is to harness these highly
distributed resources toward the development of an ultra scalable, efficient
search engine. From a technical viewpoint, any acceptable solution
must fully exploit all available resources dictating the removal of any
centralized pointsof control, which can also readily lead to performance
bottlenecks and reliability/availability problems. Equally importantly,
however, a highly distributed solution can also facilitate pluralism in informing
users about internet content, which is crucial in order to preclude
the formation of information-resource monopolies and the biased visibility
of content from economically-powerful sources. To meet these challenges,
the work described here puts forward MINERVA{\^a}{\"i}¿½{\"i}¿½, a novel search
engine architecture, designed for scalability and efficiency. MINERVA{\^a}{\"i}¿½{\"i}¿½
encompasses a suite of novel algorithms, including algorithms for creating
data networks ofinterest, placing data on network nodes, load balancing,
top-k algorithms for retrieving data at query time, and replication algorithms
for expediting top-k query processing. We have implemented the
proposed architecture and we report on our extensive experiments with
real-world, web-crawled, and synthetic data and queries, showcasing the
scalability and efficiency traits of MINERVA{\^a}{\"i}¿½{\"i}¿½.

Abstract: We consider in this paper the problem of scheduling a set of independent
parallel tasks (jobs) with respect to two criteria, namely,
the makespan (time of the last finishing job) and the minsum (average
completion time). There exist several algorithms with a good
performance guaranty for one of these criteria. We are interested
here in studying the optimization of both criteria simultaneously.
The numerical values are given for the moldable task model, where
the execution time of a task depends on the number of processors
alloted to it. The main result of this paper is to derive explicitly
a family of algorithms guaranteed for both the minsum and the
makespan. The performance guaranty of these algorithms is better
than the best algorithms known so far. The Guaranty curve
of the family is the set of all points (x; y) such that there is an
algorithm with guarantees x on makespan and y on the minsum.
When the ratio on the minsum increases, the curve tends to the
best ratio known for the makespan for moldable tasks (3=2). One
extremal point of the curves is a (3;6)-approximation algorithm.
Finally a randomized version is given, which improves this results
to (3;4.08).

Abstract: In emerging pervasive scenarios, data is collected by sensing devices in streams that occur at several distributed pointsof observation. The size of the data typically far exceeds the storage and computational capabilities of the tiny devices that have to collect and process them. A general and challenging task is to allow (some of) the nodes of a pervasive network to collectively perform monitoring of a neighbourhood ofinterest by issuing continuous aggregate queries on the streams observed in its vicinity. This class of algorithms is fully decentralized and diffusive in nature: collecting all the data at a few central nodes of the network is unfeasible in networks of low capability devices or in the presence of massive data sets. Two main problems arise in this scenario: (i) the intrinsic complexity of maintaining statistics over a data stream whose size greatly exceeds the capabilities of the device that performs the computation; (ii) composing the partial outcomes computed at different pointsof observation into an accurate, global statistic over a neighbourhood ofinterest, which entails coping with several problems, last but not least the receipt of duplicate information along multiple paths of diffusion.
Streaming techniques have emerged as powerful tools to achieve the general goals described above, in the first place because they assume a computational model in which computational and storage resources are assumed to be far exceeded by the amount of data on which computation occurs. In this contribution, we review the main streaming techniques and provide a classification of the computational problems and the applications they effectively address, with an emphasis on decentralized scenarios, which are of particular interest in pervasive networks