Mohamed Sarwat

LARS: A Location-Aware Recommender System

This paper proposes LARS, a location-aware recommender system that uses location-based ratings to produce recommendations. Traditional recommender systems do not consider spatial properties of users nor items, LARS, on the other hand, supports a taxonomy of three novel classes of location-based ratings, namely, spatial ratings for non-spatial items, non-spatial ratings for spatial items, and spatial ratings for spatial items. LARS exploits user rating locations through user partitioning, a technique that influences recommendations with ratings spatially close to querying users in a manner that maximizes system scalability while not sacrificing recommendation quality. LARS exploits item locations using travel penalty, a technique that favors recommendation candidates closer in travel distance to querying users in a way that avoids exhaustive access to all spatial items. LARS can apply these techniques separately, or in concert, depending on the type of location-based rating available. Experimental evidence using large-scale real-world data from both the Foursquare location-based social network and the Movie Lens movie recommendation system reveals that LARS is efficient, scalable, and capable of producing recommendations twice as accurate compared to existing recommendation approaches.

LARS*: An Efficient and Scalable Location-Aware Recommender System

This paper proposes LARS*, a location-aware recommender system that uses location-based ratings to produce recommendations. Traditional recommender systems do not consider spatial properties of users nor items; LARS*, on the other hand, supports a taxonomy of three novel classes of location-based ratings, namely, spatial ratings for non-spatial items, non-spatial ratings for spatial items, and spatial ratings for spatial items. LARS* exploits user rating locations through user partitioning, a technique that influences recommendations with ratings spatially close to querying users in a manner that maximizes system scalability while not sacrificing recommendation quality. LARS* exploits item locations using travel penalty, a technique that favors recommendation candidates closer in travel distance to querying users in a way that avoids exhaustive access to all spatial items. LARS* can apply these techniques separately, or together, depending on the type of location-based rating available. Experimental evidence using large-scale real-world data from both the Foursquare location-based social network and the MovieLens movie recommendation system reveals that LARS* is efficient, scalable, and capable of producing recommendations twice as accurate compared to existing recommendation approaches.

GeoSpark: a cluster computing framework for processing large-scale spatial data

This paper introduces GeoSpark an in-memory cluster computing framework for processing large-scale spatial data. GeoSpark consists of three layers: Apache Spark Layer, Spatial RDD Layer and Spatial Query Processing Layer. Apache Spark Layer provides basic Spark functionalities that include loading / storing data to disk as well as regular RDD operations. Spatial RDD Layer consists of three novel Spatial Resilient Distributed Datasets (SRDDs) which extend regular Apache Spark RDDs to support geometrical and spatial objects. GeoSpark provides a geometrical operations library that accesses Spatial RDDs to perform basic geometrical operations (e.g., Overlap, Intersect). System users can leverage the newly defined SRDDs to effectively develop spatial data processing programs in Spark. The Spatial Query Processing Layer efficiently executes spatial query processing algorithms (e.g., Spatial Range, Join, KNN query) on SRDDs. GeoSpark also allows users to create a spatial index (e.g., R-tree, Quad-tree) that boosts spatial data processing performance in each SRDD partition. Preliminary experiments show that GeoSpark achieves better run time performance than its Hadoop-based counterparts (e.g., SpatialHadoop).

Matrix Factorization with Explicit Trust and Distrust Side Information for Improved Social Recommendation

With the advent of online social networks, recommender systems have became crucial for the success of many online applications/services due to their significance role in tailoring these applications to user-specific needs or preferences. Despite their increasing popularity, in general, recommender systems suffer from data sparsity and cold-start problems. To alleviate these issues, in recent years, there has been an upsurge of interest in exploiting social information such as trust relations among users along with the rating data to improve the performance of recommender systems. The main motivation for exploiting trust information in the recommendation process stems from the observation that the ideas we are exposed to and the choices we make are significantly influenced by our social context. However, in large user communities, in addition to trust relations, distrust relations also exist between users. For instance, in Epinions, the concepts of personal “web of trust” and personal “block list” allow users to categorize their friends based on the quality of reviews into trusted and distrusted friends, respectively. Hence, it will be interesting to incorporate this new source of information in recommendation as well. In contrast to the incorporation of trust information in recommendation which is thriving, the potential of explicitly incorporating distrust relations is almost unexplored. In this article, we propose a matrix factorization-based model for recommendation in social rating networks that properly incorporates both trust and distrust relationships aiming to improve the quality of recommendations and mitigate the data sparsity and cold-start users issues. Through experiments on the Epinions dataset, we show that our new algorithm outperforms its standard trust-enhanced or distrust-enhanced counterparts with respect to accuracy, thereby demonstrating the positive effect that incorporation of explicit distrust information can have on recommender systems.

Sindbad: a location-based social networking system

This demo presents Sindbad; a location-based social networking system. Sindbad supports three new services beyond traditional social networking services, namely, location-aware news feed, location-aware recommender, and location-aware ranking. These new services not only consider social relevance for its users, but they also consider spatial relevance. Since location-aware social networking systems have to deal with large number of users, large number of messages, and user mobility, efficiency and scalability are important issues. To this end, Sindbad encapsulates its three main services inside the query processing engine of PostgreSQL. Usage and internal functionality of Sindbad, implemented with PostgreSQL and Google Maps API, are demonstrated through user (i.e., web/phone) and system analyzer GUI interfaces, respectively.

Horton: Online Query Execution Engine for Large Distributed Graphs

Graphs are used in many large-scale applications, such as social networking. The management of these graphs poses new challenges as such graphs are too large for a single server to manage efficiently. Current distributed techniques such as map-reduce and Pregel are not well-suited to processing interactive ad-hoc queries against large graphs. In this paper we demonstrate Horton, a distributed interactive query execution engine for large graphs. Horton defines a query language that allows the expression of regular language reach ability queries and provides a query execution engine with a query optimizer that allows interactive execution of queries on large distributed graphs in parallel. In the demo, we show the functionality of Horton managing a large graph for a social networking application called Codebook, whose graph represents data on software components, developers, development artifacts such as bug reports, and their interactions in large software projects.

Horton+: a distributed system for processing declarative reachability queries over partitioned graphs

Horton+ is a graph query processing system that executes declarative reachability queries on a partitioned attributed multi-graph. It employs a query language, query optimizer, and a distributed execution engine. The query language expresses declarative reachability queries, and supports closures and predicates on node and edge attributes to match graph paths. We introduce three algebraic operators, select, traverse, and join, and a query is compiled into an execution plan containing these operators. As reachability queries access the graph elements in a random access pattern, the graph is therefore maintained in the main memory of a cluster of servers to reduce query execution time. We develop a distributed execution engine that processes a query plan in parallel on the graph servers. Since the query language is declarative, we build a query optimizer that uses graph statistics to estimate predicate selectivity. We experimentally evaluate the system performance on a cluster of 16 graph servers using synthetic graphs as well as a real graph from an application that uses reachability queries. The evaluation shows (1) the efficiency of the optimizer in reducing query execution time, (2) system scalability with the size of the graph and with the number of servers, and (3) the convenience of using declarative queries.

MNTG: an extensible web-based traffic generator

Road network traffic datasets have attracted significant attention in the past decade. For instance, in spatio-temporal databases area, researchers harness road network traffic data to evaluate and validate their research. Collecting real traffic datasets is tedious as it usually takes a significant amount of time and effort. Alternatively, many researchers opt to generate synthetic traffic data using existing traffic generation tools, e.g., Brinkhoff and BerlinMOD. Unfortunately, existing road network traffic generators require significant amount of time and effort to install, configure, and run. Moreover, it is not trivial to generate traffic data in arbitrary spatial regions using existing traffic generators. In this paper, we propose Minnesota Traffic Generator (MNTG); an extensible web-based road network traffic generator that overcomes the hurdles of using existing traffic generators. MNTG does not provide a new way to simulate traffic data. Instead, it serves as a wrapper over existing traffic generators, making them easy to use, configure, and run for any arbitrary spatial road region. To generate traffic data, MNTG users just need to use its user-friendly web interface to specify an arbitrary spatial range on the map, select a traffic generator method, and submit the traffic generation request to the server. MNTG dedicated server will receive and process the submitted traffic generation request, and notify the user via email when finished. MNTG users can then download their generated data and/or visualize it on MNTG map interface. MNTG is extensible in two frontiers: (1) It can be easily extended to support various traffic generators. It is already shipped with the two most common traffic generators, Brinkhoff and BerlinMOD, yet, it also has the interface that can be used to add new traffic generators. (2) It can be easily extended to support various road network sources. It is shipped with U.S. Tiger files and Open Street Map, yet, it also has the interface that can be used to add other sources. MNTG is launched as a web service for public use; a prototype can be accessed via http://mntg.cs.umn.edu .

RecDB in action: recommendation made easy in relational databases

In this paper, we demonstrate RecDB; a full-fledged database system that provides personalized recommendation to users. We implemented RecDB using an existing open source database system PostgreSQL, and we demonstrate the effectiveness of RecDB using two existing recommendation applications (1) Restaurant Recommendation, (2) Movie Recommendation. To make the demo even more interactive, we showcase a novel application that recommends research papers presented at VLDB 2013 to the conference attendees based on their publication history in DBLP.

FAST: a generic framework for flash-aware spatial trees

Spatial tree index structures are crucial components in spatial data management systems, designed with the implicit assumption that the underlying external memory storage is the conventional magnetic hard disk drives. This assumption is going to be invalid soon, as flash memory storage is increasingly adopted as the main storage media in mobile devices, digital cameras, embedded sensors, and notebooks. Though it is direct and simple to port existing spatial tree index structures on the flash memory storage, that direct approach does not consider the unique characteristics of flash memory, i.e., slow write operations, and erase-before-update property, which would result in a sub optimal performance. In this paper, we introduce FAST (i.e., Flash-Aware Spatial Trees) as a generic framework for flash-aware spatial tree index structures. FAST distinguishes itself from all previous attempts of flash memory indexing in two aspects: (1) FAST is a generic framework that can be applied to a wide class of data partitioning spatial tree structures including R-tree and its variants, and (2) FAST achieves both efficiency and durability of read and write flash operations through smart memory flushing and crash recovery techniques. Extensive experimental results, based on an actual implementation of FAST inside the GiST index structure in PostgreSQL, show that FAST achieves better performance than its competitors.