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Davood Rafiei

University of Alberta, Computing Science, Faculty Member

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Davood Rafiei is a professor of Computing Science at the University of Alberta. His research area of interest span over databases and the Web and include integrating natural language text with relational data, Web information retrieval and similarity queries and indexing.

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Naghshe Jahan Esfahan

Yannis Manolopoulos

Aristotle University of Thessaloniki

University of Waterloo

Elakkiya Karunanihti

University of Southern California

Arizona State University

Laszlo Szirmay-Kalos

Budapest University of Technology and Economics

Stanford University

Rangachar Kasturi

University of South Florida

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Papers by Davood Rafiei

On similarity-based queries for time series data

We study a set of linear transformations on the Fourier series representation of a sequence that ... more We study a set of linear transformations on the Fourier series representation of a sequence that can be used as the basis for similarity queries on time-series data. We show that our set of transformations is rich enough to formulate operations such as moving average and time warping. We present a query processing algorithm that uses the underlying R-tree index of a multidimensional data set to answer similarity queries e ciently. Our experiments show that the performance of this algorithm is competitive to that of processing ordinary (exact match) queries using the index, and much faster than sequential scanning. We relate our transformations to the general framework for similarity queries of Jagadish et al.

Effectively visualizing large networks through sampling

Abstract We study the problem of visualizing large networks and develop techniques for effectivel... more Abstract We study the problem of visualizing large networks and develop techniques for effectively abstracting a network and reducing the size to a level that can be clearly viewed. Our size reduction techniques are based on sampling, where only a sample instead of the full network is visualized. We propose a randomized notion of" focus" that specifies a part of the network and the degree to which it needs to be magnified.

Approximately detecting duplicates for streaming data using stable bloom filters

Abstract Traditional duplicate elimination techniques are not applicable to many data stream appl... more Abstract Traditional duplicate elimination techniques are not applicable to many data stream applications. In general, precisely eliminating duplicates in an unbounded data stream is not feasible in many streaming scenarios. Therefore, we target at approximately eliminating duplicates in streaming environments given a limited space. Based on a well-known bitmap sketch, we introduce a data structure, Stable Bloom Filter, and a novel and simple algorithm.

Efficient retrieval of similar shapes

Abstract We propose an indexing technique for the fast retrieval of objects in 2D images based on... more Abstract We propose an indexing technique for the fast retrieval of objects in 2D images based on similarity between their boundary shapes. Our technique is robust in the presence of noise and supports several important notions of similarity including optimal matches irrespective of variations in orientation and/or position. Our method can also handle size-invariant matches using a normalization technique, although optimality is not guaranteed here.

On efficiently searching trajectories and archival data for historical similarities

Abstract We study the problem of efficiently evaluating similarity queries on histories, where a ... more Abstract We study the problem of efficiently evaluating similarity queries on histories, where a history is a d-dimensional time series for d≥ 1. While there are some solutions for time-series and spatio-temporal trajectories where typically d≤ 3, we are not aware of any work that examines the problem for larger values of d. In this paper, we address the problem in its general case and propose a class of summaries for histories with a few interesting properties.

Bulk loading a linear hash file

Abstract We study the problem of bulk loading a linear hash file; the problem is that a good hash... more Abstract We study the problem of bulk loading a linear hash file; the problem is that a good hash function is able to distribute records into random locations in the file; however, performing a random disk access for each record can be costly and this cost increases with the size of the file. We propose a bulk loading algorithm that can avoid random disk accesses by reducing multiple accesses to the same location into a single access and reordering the accesses such that the pages are accessed sequentially.

Efficient retrieval of similar time series

Abstract We propose an improvement of the known DFT-based indexing technique for fast retrieval o... more Abstract We propose an improvement of the known DFT-based indexing technique for fast retrieval of similar time series. We use the last few Fourier coefficients in the distance computation without storing them in the index since every coefficient at the end is the complex conjugate of a coefficient at the beginning and as strong as its counterpart. We show analytically that this observation can accelerate the search time of the index by more than a factor of two.

Efficiently evaluating order preserving similarity queries over historical market-basket data

Abstract We introduce a new domain-independent framework for formulating and efficiently evaluati... more Abstract We introduce a new domain-independent framework for formulating and efficiently evaluating similarity queries over historical data, where given a history as a sequence of timestamped observations and the pair-wise similarity of observations, we want to find similar histories. For instance, given a database of customer transactions and a time period, we can find customers with similar purchasing behaviors over this period.

Querying time series data based on similarity

Abstract We study similarity queries for time series data where similarity is defined, in a fairl... more Abstract We study similarity queries for time series data where similarity is defined, in a fairly general way, in terms of a distance function and a set of affine transformations on the Fourier series representation of a sequence. We identify a safe set of transformations supporting a wide variety of comparisons and show that this set is rich enough to formulate operations such as moving average and time scaling.

What is this page known for? Computing web page reputations

The textual content of the Web enriched with the hyperlink structure surrounding it can be a usef... more The textual content of the Web enriched with the hyperlink structure surrounding it can be a useful source of information for querying and searching. This paper presents a search process where the input is the URL of a page, and the output is a ranked set of topics on which the page has a reputation. For example, if the input is www. gamelan. com, then a possible output isJava'. We propose several algorithmic formulations of the notion of reputation using simple random walk models of Web-browsing behavior.

An autonomous page ranking method for metasearch engines

Ordering search results collected from multiple sources is a challenge to metasearch engines. We ... more Ordering search results collected from multiple sources is a challenge to metasearch engines. We present an “autonomous” ranking method, meaning one that does not depend on the individual rankings returned by the engines participating in the search. Instead, it applies our TOPIC method for evaluating the reputation of a web page on a topic [7, 6] to the problem of ranking the results to a query.(TOPIC is available at www. cs. toronto. edu/db/topic).

Diversifying web search results

Abstract Result diversity is a topic of great importance as more facets of queries are discovered... more Abstract Result diversity is a topic of great importance as more facets of queries are discovered and users expect to find their desired facets in the first page of the results. However, the underlying questions of how'diversity'interplays with'quality'and when preference should be given to one or both are not well-understood. In this work, we model the problem as expectation maximization and study the challenges of estimating the model parameters and reaching an equilibrium.

Flexible answer typing with discriminative preference ranking

An important part of question answering is ensuring a candidate answer is plausible as a response... more An important part of question answering is ensuring a candidate answer is plausible as a response. We present a flexible approach based on discriminative preference ranking to determine which of a set of candidate answers are appropriate. Discriminative methods provide superior performance while at the same time allow the flexibility of adding new and diverse features. Experimental results on a set of focused What ...? and Which ...? questions show that our learned preference ranking methods perform better than alternative solutions to the task of answer typing. A gain of almost 0.2 in MRR for both the first appropriate and first correct answers is observed along with an increase in precision over the entire range of recall.

Estimating the Number of Near-Duplicate Document Pairs for Massive Data Sets using Small Space

ABSTRACT Detecting similar or near-duplicate pairs in a large collection is an important problem ... more ABSTRACT Detecting similar or near-duplicate pairs in a large collection is an important problem with wide-spread applications; the problem has been studied for different data types (eg textual documents, spatial points and relational records) and under different settings. A more recent manifestation of the problem is efficiently finding near-duplicate Web pages, which is particularly challenging in a Web-scale because of the huge data volume and the high dimensionality of documents.

Leveraging editor collaboration patterns in Wikipedia

Abstract Predicting the positive or negative attitude of individuals towards each other in a soci... more Abstract Predicting the positive or negative attitude of individuals towards each other in a social environment has long been of interest, with applications in many domains. We investigate this problem in the context of the collaborative editing of articles in Wikipedia, showing that there is enough information in the edit history of the articles that can be utilized for predicting the attitude of co-editors.

Classifying websites into non-topical categories

Abstract With the large presence of organizations from different sectors of economy on the web, t... more Abstract With the large presence of organizations from different sectors of economy on the web, the problem of detecting to which sector a given website belongs to is both important and challenging. In this paper, we study the problem of classifying websites into four non-topical categories: public, private, non-profit and commercial franchise. Our work treats each website and all pages from the site as a single entity and classifies the entire website as opposed to a single page or a set of pages.

Bulk loading large collections of hyperlinked resources

Abstract The problem of loading large collections of hyperlinked resources into a relational data... more Abstract The problem of loading large collections of hyperlinked resources into a relational database is complicated with inter-node references when these references cannot be indexed. We show that this scenario can arise in many real life hyperlinked resources and propose several solutions to address the problem. We run some experiments over a graph of the Web with 178 million nodes and around 1 billion edges and report our results.

On MBR Approximation of Histories for Historical Queries: Expectations and Limitations

Abstract Traditional approaches for efficiently processing historical queries, where a history is... more Abstract Traditional approaches for efficiently processing historical queries, where a history is a multidimensional time-series, employ a two step filter-and-refine scheme. In the filter step, an approximation of each history often as a set of minimum bounding hyper-rectangles (MBRs) is organized using a spatial index structure such as R-tree. The index is used to prune redundant disk accesses and to reduce the number of pairwise comparisons required in the refine step.

Effectively Retrieving Related Pages on the Web using Focused Co-citation

Abstract We propose a generalization of the Co-citation algorithm, called Focused Co-citation, fo... more Abstract We propose a generalization of the Co-citation algorithm, called Focused Co-citation, for effectively retrieving related pages on the Web, where given the URL of a page, one wants to find other pages that are related or similar to the given page in some context. We identify a number of problems that frequently arise on the Web and affect the precision of algorithms that use only the link structure of the Web.

On Evaluating Similarity Queries over Historical Data

Abstract We introduce a new domain-independent framework for defining and evaluating similarity q... more Abstract We introduce a new domain-independent framework for defining and evaluating similarity queries over historical data. Given the history of an object as a sequence of observations (changes or versions) and the pair-wise similarity of observations, we want to find objects with similar histories. Our work addresses the general problem of retrieving objects with similar histories when the history cannot be modeled as a time series, hence the relevant conventional approaches are not applicable.

On similarity-based queries for time series data

We study a set of linear transformations on the Fourier series representation of a sequence that ... more We study a set of linear transformations on the Fourier series representation of a sequence that can be used as the basis for similarity queries on time-series data. We show that our set of transformations is rich enough to formulate operations such as moving average and time warping. We present a query processing algorithm that uses the underlying R-tree index of a multidimensional data set to answer similarity queries e ciently. Our experiments show that the performance of this algorithm is competitive to that of processing ordinary (exact match) queries using the index, and much faster than sequential scanning. We relate our transformations to the general framework for similarity queries of Jagadish et al.

Effectively visualizing large networks through sampling

Abstract We study the problem of visualizing large networks and develop techniques for effectivel... more Abstract We study the problem of visualizing large networks and develop techniques for effectively abstracting a network and reducing the size to a level that can be clearly viewed. Our size reduction techniques are based on sampling, where only a sample instead of the full network is visualized. We propose a randomized notion of" focus" that specifies a part of the network and the degree to which it needs to be magnified.

Approximately detecting duplicates for streaming data using stable bloom filters

Abstract Traditional duplicate elimination techniques are not applicable to many data stream appl... more Abstract Traditional duplicate elimination techniques are not applicable to many data stream applications. In general, precisely eliminating duplicates in an unbounded data stream is not feasible in many streaming scenarios. Therefore, we target at approximately eliminating duplicates in streaming environments given a limited space. Based on a well-known bitmap sketch, we introduce a data structure, Stable Bloom Filter, and a novel and simple algorithm.

Efficient retrieval of similar shapes

Abstract We propose an indexing technique for the fast retrieval of objects in 2D images based on... more Abstract We propose an indexing technique for the fast retrieval of objects in 2D images based on similarity between their boundary shapes. Our technique is robust in the presence of noise and supports several important notions of similarity including optimal matches irrespective of variations in orientation and/or position. Our method can also handle size-invariant matches using a normalization technique, although optimality is not guaranteed here.

On efficiently searching trajectories and archival data for historical similarities

Abstract We study the problem of efficiently evaluating similarity queries on histories, where a ... more Abstract We study the problem of efficiently evaluating similarity queries on histories, where a history is a d-dimensional time series for d≥ 1. While there are some solutions for time-series and spatio-temporal trajectories where typically d≤ 3, we are not aware of any work that examines the problem for larger values of d. In this paper, we address the problem in its general case and propose a class of summaries for histories with a few interesting properties.

Bulk loading a linear hash file

Abstract We study the problem of bulk loading a linear hash file; the problem is that a good hash... more Abstract We study the problem of bulk loading a linear hash file; the problem is that a good hash function is able to distribute records into random locations in the file; however, performing a random disk access for each record can be costly and this cost increases with the size of the file. We propose a bulk loading algorithm that can avoid random disk accesses by reducing multiple accesses to the same location into a single access and reordering the accesses such that the pages are accessed sequentially.

Efficient retrieval of similar time series

Abstract We propose an improvement of the known DFT-based indexing technique for fast retrieval o... more Abstract We propose an improvement of the known DFT-based indexing technique for fast retrieval of similar time series. We use the last few Fourier coefficients in the distance computation without storing them in the index since every coefficient at the end is the complex conjugate of a coefficient at the beginning and as strong as its counterpart. We show analytically that this observation can accelerate the search time of the index by more than a factor of two.

Efficiently evaluating order preserving similarity queries over historical market-basket data

Abstract We introduce a new domain-independent framework for formulating and efficiently evaluati... more Abstract We introduce a new domain-independent framework for formulating and efficiently evaluating similarity queries over historical data, where given a history as a sequence of timestamped observations and the pair-wise similarity of observations, we want to find similar histories. For instance, given a database of customer transactions and a time period, we can find customers with similar purchasing behaviors over this period.

Querying time series data based on similarity

Abstract We study similarity queries for time series data where similarity is defined, in a fairl... more Abstract We study similarity queries for time series data where similarity is defined, in a fairly general way, in terms of a distance function and a set of affine transformations on the Fourier series representation of a sequence. We identify a safe set of transformations supporting a wide variety of comparisons and show that this set is rich enough to formulate operations such as moving average and time scaling.

What is this page known for? Computing web page reputations

The textual content of the Web enriched with the hyperlink structure surrounding it can be a usef... more The textual content of the Web enriched with the hyperlink structure surrounding it can be a useful source of information for querying and searching. This paper presents a search process where the input is the URL of a page, and the output is a ranked set of topics on which the page has a reputation. For example, if the input is www. gamelan. com, then a possible output isJava'. We propose several algorithmic formulations of the notion of reputation using simple random walk models of Web-browsing behavior.

An autonomous page ranking method for metasearch engines

Ordering search results collected from multiple sources is a challenge to metasearch engines. We ... more Ordering search results collected from multiple sources is a challenge to metasearch engines. We present an “autonomous” ranking method, meaning one that does not depend on the individual rankings returned by the engines participating in the search. Instead, it applies our TOPIC method for evaluating the reputation of a web page on a topic [7, 6] to the problem of ranking the results to a query.(TOPIC is available at www. cs. toronto. edu/db/topic).

Diversifying web search results

Abstract Result diversity is a topic of great importance as more facets of queries are discovered... more Abstract Result diversity is a topic of great importance as more facets of queries are discovered and users expect to find their desired facets in the first page of the results. However, the underlying questions of how'diversity'interplays with'quality'and when preference should be given to one or both are not well-understood. In this work, we model the problem as expectation maximization and study the challenges of estimating the model parameters and reaching an equilibrium.

Flexible answer typing with discriminative preference ranking

An important part of question answering is ensuring a candidate answer is plausible as a response... more An important part of question answering is ensuring a candidate answer is plausible as a response. We present a flexible approach based on discriminative preference ranking to determine which of a set of candidate answers are appropriate. Discriminative methods provide superior performance while at the same time allow the flexibility of adding new and diverse features. Experimental results on a set of focused What ...? and Which ...? questions show that our learned preference ranking methods perform better than alternative solutions to the task of answer typing. A gain of almost 0.2 in MRR for both the first appropriate and first correct answers is observed along with an increase in precision over the entire range of recall.

Estimating the Number of Near-Duplicate Document Pairs for Massive Data Sets using Small Space

ABSTRACT Detecting similar or near-duplicate pairs in a large collection is an important problem ... more ABSTRACT Detecting similar or near-duplicate pairs in a large collection is an important problem with wide-spread applications; the problem has been studied for different data types (eg textual documents, spatial points and relational records) and under different settings. A more recent manifestation of the problem is efficiently finding near-duplicate Web pages, which is particularly challenging in a Web-scale because of the huge data volume and the high dimensionality of documents.

Leveraging editor collaboration patterns in Wikipedia

Abstract Predicting the positive or negative attitude of individuals towards each other in a soci... more Abstract Predicting the positive or negative attitude of individuals towards each other in a social environment has long been of interest, with applications in many domains. We investigate this problem in the context of the collaborative editing of articles in Wikipedia, showing that there is enough information in the edit history of the articles that can be utilized for predicting the attitude of co-editors.

Classifying websites into non-topical categories

Abstract With the large presence of organizations from different sectors of economy on the web, t... more Abstract With the large presence of organizations from different sectors of economy on the web, the problem of detecting to which sector a given website belongs to is both important and challenging. In this paper, we study the problem of classifying websites into four non-topical categories: public, private, non-profit and commercial franchise. Our work treats each website and all pages from the site as a single entity and classifies the entire website as opposed to a single page or a set of pages.

Bulk loading large collections of hyperlinked resources

Abstract The problem of loading large collections of hyperlinked resources into a relational data... more Abstract The problem of loading large collections of hyperlinked resources into a relational database is complicated with inter-node references when these references cannot be indexed. We show that this scenario can arise in many real life hyperlinked resources and propose several solutions to address the problem. We run some experiments over a graph of the Web with 178 million nodes and around 1 billion edges and report our results.

On MBR Approximation of Histories for Historical Queries: Expectations and Limitations

Abstract Traditional approaches for efficiently processing historical queries, where a history is... more Abstract Traditional approaches for efficiently processing historical queries, where a history is a multidimensional time-series, employ a two step filter-and-refine scheme. In the filter step, an approximation of each history often as a set of minimum bounding hyper-rectangles (MBRs) is organized using a spatial index structure such as R-tree. The index is used to prune redundant disk accesses and to reduce the number of pairwise comparisons required in the refine step.

Effectively Retrieving Related Pages on the Web using Focused Co-citation

Abstract We propose a generalization of the Co-citation algorithm, called Focused Co-citation, fo... more Abstract We propose a generalization of the Co-citation algorithm, called Focused Co-citation, for effectively retrieving related pages on the Web, where given the URL of a page, one wants to find other pages that are related or similar to the given page in some context. We identify a number of problems that frequently arise on the Web and affect the precision of algorithms that use only the link structure of the Web.

On Evaluating Similarity Queries over Historical Data

Abstract We introduce a new domain-independent framework for defining and evaluating similarity q... more Abstract We introduce a new domain-independent framework for defining and evaluating similarity queries over historical data. Given the history of an object as a sequence of observations (changes or versions) and the pair-wise similarity of observations, we want to find objects with similar histories. Our work addresses the general problem of retrieving objects with similar histories when the history cannot be modeled as a time series, hence the relevant conventional approaches are not applicable.