Pinar Karagoz

A Novel Wind Power Forecast Model: Statistical Hybrid Wind Power Forecast Technique (SHWIP)

As the result of increasing population and growing technological activities, nonrenewable energy sources, which are the main energy providers, are diminishing day by day. Due to this factor, efforts on efficient utilization of renewable energy sources have increased all over the world. Wind is one of the most significant alternative energy resources. However, in comparison with other renewable energy sources, it is so variable that there is a need for estimating and planning of wind power generation. In this paper, a new statistical short-term (up to 48 h) wind power forecast model, namely statistical hybrid wind power forecast technique (SHWIP), is presented. In the proposed model, weather events are clustered with respect to the most important weather forecast parameters. It also combines the power forecasts obtained from three different numerical weather prediction (NWP) sources and produces a hybridized final forecast. The proposed model has been in operation at the Wind Power Monitoring and Forecast System for Turkey (RITM), and the results of the new model are compared with well-known statistical models and physical models in the literature. The most important characteristics of the proposed model is the need for a lesser amount of historical data while constructing the mathematical model compared with the other statistical models such as artificial neural networks (ANN) and support vector machine (SVM). To produce a reliable forecast, ANN and SVM need at least 1 year of historical data; on the other hand, the proposed SHWIP methods results are applicable even under 1 month of training data, and this is an important feature for the forecast of the newly established wind power plants (WPPs).

Design and Implementation of a Distributed Workflow Management System: METUFlow

Workflows are activities involving the coordinated execution of multiple tasks performed by different processing entities, mostly in distributed heterogeneous environments which are very common in enterprises of even moderate complexity. Centralized workflow systems fall short to meet the demands of such environments.

Context-Aware Friend Recommendation for Location Based Social Networks using Random Walk

The location-based social networks (LBSN) facilitate users to check-in their current location and share it with other users. The accumulated check-in data can be employed for the benefit of users by providing personalized recommendations. In this paper, we propose a random walk based context-aware friend recommendation algorithm (RWCFR). RWCFR considers the current context (i.e. current social relations, personal preferences and current location) of the user to provide personalized recommendations. Our LBSN model is an undirected unweighted graph model that represents users, locations, and their relationships. We build a graph according to the current context of the user depending on this LBSN model. In order to rank the recommendation scores of the users for friend recommendation, a random walk with restart approach is employed. We compare RWCFR with popularity-based, friend-based and expert-based baseline approaches. According to the results, our friend recommendation algorithm outperforms these approaches in all the tests.

Evidential location estimation for events detected in Twitter

Event detection from microblogs and social networks, especially from Twitter, is an active and rich research topic. By grouping similar tweets in clusters, people can extract events and follow the happenings in a community. In this work, we focus on estimating the geographical locations of events that are detected in Twitter. An important novelty of our work is the application of evidential reasoning techniques, namely the Demspter-Shafer Theory (DST), for this problem. By utilizing several features of tweets, we aim to produce belief intervals for a set of possible discrete locations. DST helps us deal with uncertainties, assign belief values to subsets of solutions, and combine pieces of evidence obtained from different tweet features. The initial results on several real cases suggest the applicability and usefulness of DST for the problem.

CRoM and HuspExt: Improving Efficiency of High Utility Sequential Pattern Extraction

High utility sequential pattern mining has been considered as an important research problem and a number of relevant algorithms have been proposed for this topic. The main challenge of high utility sequential pattern mining is that, the search space is large and the efficiency of the solutions is directly affected by the degree at which they can eliminate the candidate patterns. Therefore, the efficiency of any high utility sequential pattern mining solution depends on its ability to reduce this big search space, and as a result, lower the computational complexity of calculating the utilities of the candidate patterns. In this paper, we propose efficient data structures and pruning technique which is based on Cumulated Rest of Match (CRoM) based upper bound. CRoM, by defining a tighter upper bound on the utility of the candidates, allows more conservative pruning before candidate pattern generation in comparison to the existing techniques. In addition, we have developed an efficient algorithm, High Utility Sequential Pattern Extraction (HuspExt), which calculates the utilities of the child patterns based on that of the parents’. Substantial experiments on both synthetic and real datasets from different domains show that, the proposed solution efficiently discovers high utility sequential patterns from large scale datasets with different data characteristics, under low utility thresholds.

Enhancing and abstracting scientific workflow provenance for data publishing

Many scientists are using workflows to systematically design and run computational experiments. Once the workflow is executed, the scientist may want to publish the dataset generated as a result, to be, e.g., reused by other scientists as input to their experiments. In doing so, the scientist needs to curate such dataset by specifying metadata information that describes it, e.g. its derivation history, origins and ownership. To assist the scientist in this task, we explore in this paper the use of provenance traces collected by workflow management systems when enacting workflows. Specifically, we identify the shortcomings of such raw provenance traces in supporting the data publishing task, and propose an approach whereby distilled, yet more informative, provenance traces that are fit for the data publishing task can be derived.

Context-aware location recommendation by using a random walk-based approach

The location-based social networks (LBSN) enable users to check in their current location and share it with other users. The accumulated check-in data can be employed for the benefit of users by providing personalized recommendations. In this paper, we propose a context-aware location recommendation system for LBSNs using a random walk approach. Our proposed approach considers the current context (i.e., current social relations, personal preferences and current location) of the user to provide personalized recommendations. We build a graph model of LBSNs for performing a random walk approach with restart. Random walk is performed to calculate the recommendation probabilities of the nodes. A list of locations are recommended to users after ordering the nodes according to the estimated probabilities. We compare our algorithm, CLoRW, with popularity-based, friend-based and expert-based baselines, user-based collaborative filtering approach and a similar work in the literature. According to experimental results, our algorithm outperforms these approaches in all of the test cases.

Improved Genetic Algorithm Based Approach for QoS Aware Web Service Composition

Use of web services is one of the most rapidly developing technologies. Since web services are defined by XML- based standards to overcome platform dependency, they are very eligible to integrate with each other in order to establish new services. This composition enables us to reuse existing services, which results in less cost and time consumption. One of the recent problems with web service composition is to maximize the overall Quality of Service (QoS) of the composed service. Most common elements of QoS are response time, availability, reliability, throughput and cost (price). Since the selection of the optimal execution plan that maximizes the compositions overall QoS is a NP hard problem, applying optimization techniques is very popular. In this work, we propose an improved Genetic Algorithm based approach to optimize the overall QoS of the composed service. Experimental results indicate improvement for QoS of the composition built by the proposed methods.

Extended feature combination model for recommendations in location-based mobile services

With the increasing availability of location-based services, location-based social networks and smart phones, standard rating schema of recommender systems that involve user and item dimensions is extended to three-dimensional (3-D) schema involving context information. Although there are models proposed for dealing with data in this form, the problem of combining it with additional features and constructing a general model suitable for different forms of recommendation system techniques has not been fully explored. This work proposes a technique to reduce 3-D rating data into 2-D for two reasons: employing already developed efficient methods for 2-D on a 3-D data and expanding it with additional features, which are usually 2-D also, if it is necessary. Our experiments show that this reduction is effective. The proposed 2-D model supports content-based, collaborative filtering and hybrid recommendation approaches effectively, whereas we have achieved the best accuracy results for pure collaborative filtering recommendation model. Since our method was built on efficient singular value decomposition-based dimension reduction idea, it also works very efficiently, and in our experiments, we have obtained better run-time results than standard methods developed for 3-D data using higher-order singular value decomposition.

LabelFlow: Exploiting Workflow Provenance to Surface Scientific Data Provenance

Provenance traces captured by scientific workflows can be useful for designing, debugging and maintenance. However, our experience suggests that they are of limited use for reporting results, in part because traces do not comprise domain-specific annotations needed for explaining results, and the black-box nature of some workflow activities. We show that by basic mark-up of the data processing within activities and using a set of domain specific label generation functions, standard workflow provenance can be utilised as a platform for the labelling of data artefacts. These labels can in turn aid selection of data subsets and proxy for data descriptors for shared datasets.