Efthymia Tsamoura

Automatic Color Based Reassembly of Fragmented Images and Paintings

The problem of reassembling image fragments arises in many scientific fields, such as forensics and archaeology. In the field of archaeology, the pictorial excavation findings are almost always in the form of painting fragments. The manual execution of this task is very difficult, as it requires great amount of time, skill and effort. Thus, the automation of such a work is very important and can lead to faster, more efficient, painting reassembly and to a significant reduction in the human effort involved. In this paper, an integrated method for automatic color based 2-D image fragment reassembly is presented. The proposed 2-D reassembly technique is divided into four steps. Initially, the image fragments which are probably spatially adjacent, are identified utilizing techniques employed in content based image retrieval systems. The second operation is to identify the matching contour segments for every retained couple of image fragments, via a dynamic programming technique. The next step is to identify the optimal transformation in order to align the matching contour segments. Many registration techniques have been evaluated to this end. Finally, the overall image is reassembled from its properly aligned fragments. This is achieved via a novel algorithm, which exploits the alignment angles found during the previous step. In each stage, the most robust algorithms having the best performance are investigated and their results are fed to the next step. We have experimented with the proposed method using digitally scanned images of actual torn pieces of paper image prints and we produced very satisfactory reassembly results.

Querying with access patterns and integrity constraints

Traditional query processing involves a search for plans formed by applying algebraic operators on top of primitives representing access to relations in the input query. But many querying scenarios involve two interacting issues that complicate the search. On the one hand, the search space may be limited by access restrictions associated with the interfaces to datasources, which require certain parameters to be given as inputs. On the other hand, the search space may be extended through the presence of integrity constraints that relate sources to each other, allowing for plans that do not match the structure of the user query. In this paper we present the first optimization approach that attacks both these difficulties within a single framework, presenting a system in which classical cost-based join optimization is extended to support both access-restrictions and constraints. Instead of iteratively exploring subqueries of the input query, our optimizer explores a space of proofs that witness the answering of the query, where each proof has a direct correspondence with a query plan.

Decentralized execution of linear workflows over web services

The development of workflow management systems (WfMSs) for the effective and efficient management of workflows in wide-area infrastructures has received a lot of attention in recent years. Existing WfMSs provide tools that simplify the workflow composition and enactment actions, while they support the execution of complex tasks on remote computational resources usually through calls to web services (WSs). Nowadays, an increasing number of WfMSs employ pipelining during the workflow execution. In this work, we focus on improving the performance of long-running workflows consisting of multiple pipelined calls to remote WSs when the execution takes place in a totally decentralized manner. The novelty of our algorithm lies in the fact that it considers the network heterogeneity, and although the optimization problem becomes more complex, it is capable of finding an optimal solution in a short time. Our proposal is evaluated through a real prototype deployed on PlanetLab, and the experimental results are particularly encouraging.

Benchmarking the Chase

The chase is a family of algorithms used in a number of data management tasks, such as data exchange, answering queries under dependencies, query reformulation with constraints, and data cleaning. It is well established as a theoretical tool for understanding these tasks, and in addition a number of prototype systems have been developed. While individual chase-based systems and particular optimizations of the chase have been experimentally evaluated in the past, we provide the first comprehensive and publicly available benchmark---test infrastructure and a set of test scenarios---for evaluating chase implementations across a wide range of assumptions about the dependencies and the data. We used our benchmark to compare chase-based systems on data exchange and query answering tasks with one another, as well as with systems that can solve similar tasks developed in closely related communities. Our evaluation provided us with a number of new insights concerning the factors that impact the performance of chase implementations.

Generating low-cost plans from proofs

We look at generating plans that answer queries over restricted interfaces, making use of information about source integrity constraints, access restrictions, and access costs. Our method can exploit the integrity constraints to find low-cost access plans even when there is no direct access to relations appearing in the query. The key idea of our method is to move from a search for a plan to a search for a proof that a query is answerable, and then \emph{generate a plan from a proof}. Discovery of one proof allows us to find a single plan that answers the query; exploration of several alternative proofs allows us to find low-cost plans. We start by overviewing a correspondence between proofs and restricted-interface plans in the context of arbitrary first-order constraints, based on interpolation. The correspondence clarifies the connection between preservation and interpolation theorems and reformulation problems, while generalizing it in several dimensions. We then provide direct plan-generation algorithms for schemas based on tuple-generating dependencies. Finally, we show how the direct plan-generation approach can be adapted to take into account the cost of plans.

Generating Plans from Proofs: The Interpolation-based Approach to Query Reformulation

Abstract Query reformulation refers to a process of translating a source query—a request for information in some high-level logic-based language—into a target plan that abides by certain interface ...

PDQ: proof-driven query answering over web-based data

The data needed to answer queries is often available through Web-based APIs. Indeed, for a given query there may be many Web-based sources which can be used to answer it, with the sources overlapping in their vocabularies, and differing in their access restrictions (required arguments) and cost. We introduce PDQ (Proof-Driven Query Answering), a system for determining a query plan in the presence of web-based sources. It is: (i) constraint-aware -- exploiting relationships between sources to rewrite an expensive query into a cheaper one, (ii) access-aware -- abiding by any access restrictions known in the sources, and (iii) cost-aware -- making use of any cost information that is available about services. PDQ takes the novel approach of generating query plans from proofs that a query is answerable. We demonstrate the use of PDQ and its effectiveness in generating low-cost plans.

Generating Plans from Proofs

We present algorithms for answering queries making use of information about source integrity constraints, access restrictions, and access costs. Our method can exploit the integrity constraints to find plans even when there is no direct access to relations appearing in the query. We look at different kinds of plans, depending on the kind of relational operators that are permitted within their commands. To each type of plan, we associate a semantic property that is necessary for having a plan of that type. The key idea of our method is to move from a search for a plan to a search for a proof of the corresponding semantic property, and then generate a plan from a proof. We provide algorithms for converting proofs to plans and show that they will find a plan of the desired type whenever such a plan exists. We show that while discovery of one proof allows us to find a single plan that answers the query, we can explore alternative proofs to find lower-cost plans.

Multi-objective optimization of data flows in a multi-cloud environment

As cloud-based solutions have become one of the main choices for intensive data analysis both for business decision making and scientific purposes, users face the problem of choosing among different cloud providers. In this work, we deal with data analysis flows that can be split in stages, and each stage can run on multiple cloud infrastructures. For each stage, a cloud provider may make a bid in the form of a continuous function in the time delay-monetary cost domain. The goal is to compute the optimal combination of bids according to how much a user is prepared to pay for the total time delay to execute the analysis task. The contributions of this work are (i) to provide a solution that can be computed in pseudo-polynomial time and with bounded relative error for the generic case; (ii) to provide exact polynomial solutions for specific cases; and (iii) to experimentally evaluate our proposal against other techniques. Our extensive results show that we can yield improvements up to an order of magnitude compared to existing heuristics.

Optimal Service Ordering in Decentralized Queries Over Web Services

The problem of ordering expensive predicates (or filter ordering) has recently received renewed attention due to emerging computing paradigms such as processing engines for queries over remote Web Services, and cloud and grid computing. The optimization of pipelined plans over services differs from traditional optimization significantly, since execution takes place in parallel and thus the query response time is determined by the slowest node in the plan, which is called the bottleneck node. Although polynomial algorithms have been proposed for several variants of optimization problems in this setting, the fact that communication links are typically heterogeneous in wide-area environments has been largely overlooked. The authors propose an attempt to optimize linear orderings of services when the services communicate directly with each other and the communication links are heterogeneous. The authors propose a novel optimal algorithm to solve this problem efficiently. The evaluation of the proposal shows that it can result in significant reductions of the response time.