published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy

Η ολοένα αυξανόμενη διείσδυση και χρήση του Internet παγκοσμίως έχει οδηγήσει στην επιτακτική ανάγκη ενός καλά ορισμένου και κοινά αποδεκτού τρόπου αναπαράστασης και ανταλλαγής της πληροφορίας στο διαδίκτυο. Όλο και περισσότερα ετερογενή συστήματα και πλατφόρμες χρειάζονται να ανταλλάξουν δεδομένα και πληροφορίες μεταξύ τους, με τρόπο καλά ορισμένο αλλά ταυτόχρονα δυναμικό και ελαστικό. H XML αναπτύχθηκε για να επιλύσει ακριβώς αυτό το πρόβλημα, της εινιαίας και καθολικά αποδεκτής αναπαράστασης της διακινούμενης πληροφορίας. Η ραγδαία αύξηση όμως του όγκου των δεδομένων που αναπαρίστανται σε XML δημιούργησε την ανάγκη αναζήτησης μέσα στην δενδρική δομή ενός ΧΜL εγγράφου για κάποια συγκεκριμένη πληροφορία. Η ανάγκη αυτή ταυτόχρονα με την ανάγκη για γρήγορη πρόσβαση στους κόμβους του ΧΜL δέντρου, οδήγησε σε διάφορα εξειδικευμένα ευρετήρια καθένα με διαφορετικά χαρακτηριστικά και δομή. Τα δεδομένα όμως στη σύγχρονη κοινωνία της πληροφόρησης δεν παραμένουν στατικά, αλλά διαρκώς αλλάζουν και μεταβάλλονται δυναμικά. Για να μπορέσουν να αναταποκριθούν στη δυναμική αυτή των δεδομένων, τα ευρετήρια θα πρέπει να έχουν τη δυνατότητα να μεταβάλλονται και αυτά δυναμικά και με ελάχιστο κόστος. Ταυτόχρονα με την ανάγκη αναζήτησης συγκεκριμένης πληροφορίας μέσα σε ένα σύνολο XML δεδομένων, γεννήθηκε και η ακριβώς αντίστροφη ανάγκη: το φιλτράρισμα ενός συνόλου XML δεδομένων διαμέσου κάποιων προτύπων και κανόνων ώστε να βρεθούν εκείνα τα δεδομένα που ταιριάζουν με τα αποθηκευμένα πρότυπα και κανόνες. Το πρόβλημα αυτό συναντάται κυρίως στα συστήματα publish/subscribe, στα οποία οι χρήστες ορίζουν τα ενδιαφέροντά τους και το σύστημα αναλαμβάνει να τους αποστέλλει μόνο πληροφορίες και δεδομένα σχετικά με τις προτιμήσεις τους. Η αναπαράσταση της πληροφορίας σε XML οδήγησε τα συστήματα αυτά να ενσωματώσουν αλγορίθμους φιλτραρίσματος των XML δεδομένων διαμέσου ενός συνόλου προτύπων και κανόνων που έχουν ορίσει εκ των προτέρων οι χρήστες του. Στα πλαίσια της μεταπτυχιακής αυτής εργασίας μελετάμε και συγκρίνουμε τις υπάρχουσες τεχνικές δυναμικής δεικτοδότησης και φιλτραρίσματος XML εγγράφων και παρουσιάζουμε έναν νέο, πρωτοποριακό αλγόριθμο φιλτραρίσματος που υπερτερεί των υπαρχόντων. / The increasingly use of Internet worldwide has led to the impulsory need of a standard, well-defined and wide-accepted information representation. More and more heterogeneous systems require exchanging data and information between them, in a well-defined, flexible and dynamic matter. XML was created in order to address the need of a standard and well-defined information representation. However, the boom of information capacity expressed in XML has rised a new problem: that of searching among a huge XML data for a specific information. This problem along with the need of efficient access to the nodes of an XML tree-structured document has led to the proposal of various heuristic indexes, each one with diferrent structure and features. However, because data do not remain static but change dynamically through time, the proposed indexes should have the flexibility to change dynamically accordingly to the data changes, with the minimal cost. Together with the problem of searching through a set of XML documents, a new problem has rised: that of filtering an XML document through a predefined set of patterns and rules. This problem comes from publish/subscribe systems where the users define their interests and preferences and the system undertakes to filter incoming information through the stored user profiles. The vast volume of XML-represented data and information has led those systems to incorporate XML filtering algorithms which filter the incoming XML data streams through the predefined user profiles.

Comparative analysis of genomic data investigates the relationship of genome structure and function across different biological species to shed light on their similarities and differences. In this dissertation, we study two important problems in comparative genomics, namely comparative sequence analysis and comparative network analysis. In the comparative sequence analysis, we study the multiple sequence alignment of protein and DNA sequences as well as the structural alignment of multiple RNA sequences. For closely related sequences, multiple sequence alignment can be efficiently performed through progressive techniques. However, for divergent sequences it is very challenging to predict an accurate alignment. Here, we introduce PicXAA, an efficient non-progressive technique for multiple protein and DNA sequence alignment. We also further extend PicXAA to PicXAA-R for structural alignment of RNA sequences. PicXAA and PicXAA-R greedily build up the alignment from sequence regions with high local similarity, thereby yielding an accurate global alignment that effectively captures local similarities among sequences. As another important research area in comparative genomics, we also investigate the comparative network analysis problem. Translation of increasing number of large-scale biological networks into meaningful biological insights requires efficient computational techniques. One such example is network querying, which aims to identify subnetwork regions in a large target network that are similar to a given query network. Here, we introduce an efficient algorithm for querying large-scale biological networks, called RESQUE. RESQUE adopts a semi-Markov random walk model to probabilistically estimate the correspondence scores between nodes that belong to different networks. The target network is iteratively reduced based on the estimated correspondence scores until the best matching subnetwork emerges. The proposed network querying scheme is computationally efficient, can handle any network query with an arbitrary topology, and yields accurate querying results. We also extend the idea used in RESQUE to develop an efficient algorithm for alignment of multiple large-scale biological networks, called SMETANA. SMETANA outperforms state-of- the-art network alignment techniques, in terms of both computational efficiency and alignment accuracy. The accomplished studies have enabled us to provide a coherent framework for probabilistic approach to comparative analysis of biological sequences and networks. Such a probabilistic framework helps us employ rigorous mathematical schemes to find accurate and efficient solutions to these problems.

Thesis (M.S.)--Brigham Young University. Dept. of Computer Science, 2006. / Includes bibliographical references (p. 81-85).

Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (p. 169-179).

Thesis (Ph.D.) in Spatial Information Science and Engineering--University of Maine, 2009. / Includes vita. Includes bibliographical references (leaves 185-202).

Thesis (M.S.)--Ohio University, November, 2006. / Title from PDF t.p. Includes bibliographical references.

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In the past few years, ridesharing has been becoming increasingly popular in urban areas worldwide for its low cost and environment friendliness. In a typical scenario, the ridesharing service provider matches drivers of private vehicles or taxis to those seeking local taxicab- like transportation. Much research attention has been drawn to the optimization of travel costs in shared rides. However, other important factors in ridesharing, such as the social comfort, trust issues and revenue, have not been fully considered in the existing works. Social-aware ridesharing, which makes use of social relations among drivers and riders to address safety issues, and dynamic pricing, which dynamically determines shared ride fares, are two active research directions with important business implications. In this dissertation, we take the first step to comprehensively investigate the social-aware ridesharing queries. First, we study the problem of the top-k social-aware taxi ridesharing query. In particular, upon receiving a user's trip request, the service ranks feasible taxis in a way that integrates detour in time and passengers' cohesion in social distance. We propose a new system framework to support such a social-aware taxi-sharing service. It provides two methods for selecting candidate taxis for a given trip request. The grid-based method quickly goes through available taxis and returns a relatively larger candidate set, whereas the edge-based method takes more time to obtain a smaller candidate set. Furthermore, we design techniques to speed up taxi route scheduling for a given trip request. We propose travel-time based bounds to rule out unqualified cases quickly, as well as algorithms to find feasible cases efficiently. We evaluate our proposals using a real taxi dataset from New York City. Experimental results demonstrate the efficiency and scalability of the proposed taxi recommendation solution in real-time social-aware ridesharing services. Second, we study the problem of efficient matching of offers and requests in social-aware ridesharing. We formulate a new problem, named Assignment of Requests to Offers (ARO), that aims to maximize the number of served riders while satisfying the social comfort constraints as well as spatial-temporal constraints. We prove that the ARO problem is NP- hard. We then propose an exact algorithm for a simplified ARO problem. We further propose three pruning strategies to efficiently narrow down the searching space and speed up the assignment processing. Based on these pruning strategies, we develop two novel heuristic algorithms, the request-oriented approach and offer-oriented approach, to tackle the ARO problem. We also study the dynamic ARO problem and present a novel algorithm to tackle this problem. Through extensive experiments, we demonstrate the efficiency and effectiveness of our proposed approaches on real-world datasets. Third, we study the top-k vehicle matching in social ridesharing. In the current ridesharing research, optimizing social cohesion and revenue at the same time has not been well studied. We present a new pricing scheme that better incentivizes drivers and riders to participate in ridesharing, and then propose a novel type of Price-aware Top-k Matching (PTkM) queries which retrieve the top-k vehicles for a rider's request by taking into account both social relations and revenue. We design an efficient algorithm with a set of powerful pruning techniques to tackle this problem. Moreover, we propose a novel index tailored to our problem to further speed up query processing. Extensive experimental results on real datasets show that our proposed algorithms achieve desirable performance for real-world deployment. The work of this thesis shows that the social-aware ridesharing query processing techniques are effective and efficient, which would facilitate ridesharing services in real world.

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