Ahmed R. Mahmood

Tornado: a distributed spatio-textual stream processing system

The widespread use of location-aware devices together with the increased popularity of micro-blogging applications (e.g., Twitter) led to the creation of large streams of spatio-textual data. In order to serve real-time applications, the processing of these large-scale spatio-textual streams needs to be distributed. However, existing distributed stream processing systems (e.g., Spark and Storm) are not optimized for spatial/textual content. In this demonstration, we introduce Tornado, a distributed in-memory spatio-textual stream processing server that extends Storm. To efficiently process spatio-textual streams, Tornado introduces a spatio-textual indexing layer to the architecture of Storm. The indexing layer is adaptive, i.e., dynamically re-distributes the processing across the system according to changes in the data distribution and/or query workload. In addition to keywords, higher-level textual concepts are identified and are semantically matched against spatio-textual queries. Tornado provides data deduplication and fusion to eliminate redundant textual data. We demonstrate a prototype of Tornado running against real Twitter streams, where the users can register continuous or snapshot spatio-textual queries using a map-assisted query-interface.

Defending against energy efficient link layer jamming denial of service attack in wireless sensor networks

Wireless Sensor Networks (WSNs) are vulnerable to attacks due to their limited software and hardware capabilities. Energy efficient link layer jamming attacks have proven to be a real threat to wireless sensor networks. This type of attacks is able to prevent network communication for relatively long periods as it does not spend a lot of power while jamming. This paper proposes and evaluates two modifications to the Lightweight Medium Access Control (LMAC) [3] protocol. The first is Data Packet Separation Slot Size Randomization (DS-SSR); the second is Round Robin (RR) slot size assignment. The paper shows that (DS-SSR) can increase the WSN resistance against the Energy efficient denial of service link layer jamming attacks. The paper also shows that employing RR slightly eliminates the negative impact on the network throughput when using countermeasures against energy efficient jamming. Two measures are used to evaluate the resistance of the proposed protocol against the attack: lifetime advantage and censorship rate. Experimental results show that about 8 % reduction of the attacker lifetime advantage can be achieved with DS-SSR LMAC compared to other countermeasure. Furthermore, results show that the censorship rate of our proposed protocol was similar to the other countermeasures.

CRIS — Computational research infrastructure for science

The challenges facing the scientific community are common and real: conduct relevant and verifiable research in a rapidly changing collaborative landscape with an ever increasing scale of data. It has come to a point where research activities cannot scale at the rate required without improved cyberinfrastructure (CI). In this paper we describe CRIS (The Computational Research Infrastructure for Science), with its primary tenets to provide an easy to use, scalable, and collaborative scientific data management and workflow cyberinfrastructure for scientists lacking extensive computational expertise. Some of the key features of CRIS are: 1) semantic definition of scientific data using domain vocabularies; 2) embedded provenance for all levels of research activity (data, workflows, tools etc.); 3) easy integration of existing heterogeneous data and computational tools on local or remote computers; 4) automatic data quality monitoring for syntactic and domain standards; and 5) shareable yet secure access to research data, computational tools and equipment. CRIS currently has a community of users in Agronomy, Biochemistry, Bioinformatics and Healthcare Engineering at Purdue University (cris.cyber.purdue.edu).

A demonstration of AQWA: adaptive query-workload-aware partitioning of big spatial data

The ubiquity of location-aware devices, e.g., smartphones and GPS devices, has led to a plethora of location-based services in which huge amounts of geotagged information need to be efficiently processed by large-scale computing clusters. This demo presents AQWA, an adaptive and query-workload-aware data partitioning mechanism for processing large-scale spatial data. Unlike existing cluster-based systems, e.g., SpatialHadoop, that apply static partitioning of spatial data, AQWA has the ability to react to changes in the query-workload and data distribution. A key feature of AQWA is that it does not assume prior knowledge of the query-workload or data distribution. Instead, AQWA reacts to changes in both the data and the query-workload by incrementally updating the partitioning of the data. We demonstrate two prototypes of AQWA deployed over Hadoop and Spark. In both prototypes, we process spatial range and k-nearest-neighbor (kNN, for short) queries over large-scale spatial datasets, and we exploit the performance of AQWA under different query-workloads.

Query Processing Techniques for Big Spatial-Keyword Data

The widespread use of GPS-enabled cellular devices, i.e., smart phones, led to the popularity of numerous mobile applications, e.g., social networks, micro-blogs, mobile web search, and crowd-powered reviews. These applications generate large amounts of geo-tagged textual data, i.e., spatial-keyword data. This data needs to be processed and queried at an unprecedented scale. The management of spatial-keyword data at this scale goes beyond the capabilities of centralized systems. We live in the era of big data and the big data model is currently been used to address scalability issues in various application domains. This has led to the development of various big spatial-keyword processing systems. These systems are designed to ingest, store, index, and query huge amounts of spatial-keyword data. In this 1.5 hour tutorial, we explore recent research efforts in the area of big spatial-keyword processing. First, we give main motivations behind big spatial-keyword systems with real-life applications. We describe the main models for big spatial-keyword processing, and list the popular spatial-keyword queries. Then, we present the approaches that have been adopted in big spatial-keyword processing systems with special attention to data indexing and spatial and keyword data partitioning. Finally, we conclude this tutorial with a discussion on some of the open problems and research directions in the area of big spatial-keyword query processing.

Atlas: on the expression of spatial-keyword group queries using extended relational constructs

The popularity of GPS-enabled cellular devices introduced numerous applications, e.g., social networks, micro-blogs, and crowd-powered reviews. These applications produce large amounts of geo-tagged textual data that need to be processed and queried. Nowadays, many complex spatio-textual operators and their matching complex indexing structures are being proposed in the literature to process this spatio-textual data. For example, there exist several complex variations of the spatio-textual group queries that retrieve groups of objects that collectively satisfy certain spatial and textual criteria. However, having complex operators is against the spirit of SQL and relational algebra. In contrast to these complex spatio-textual operators, in relational algebra, simple relational operators are offered, e.g., relational selects, projects, order by, and group by, that are composable to form more complex queries. In this paper, we introduce Atlas, an SQL extension to express complex spatial-keyword group queries. Atlas follows the philosophy of SQL and relational algebra in that it uses simple declarative spatial and textual building-block operators and predicates to extend SQL. Not only that Atlas can represent spatio-textual group queries from the literature, but also it can compose other important queries, e.g., retrieve spatio-textual groups from subsets of object datasets where the selected subset satisfies user-defined relational predicates and the groups of close-by objects contain miss-spelled keywords. We demonstrate that Atlas is able to represent a wide range of spatial-keyword queries that existing indexes and algorithms would not be able to address. The building- block paradigm adopted by Atlas creates room for query optimization, where multiple query execution plans can be formed.

Indexing recent trajectories of moving objects

The plethora of lacation-aware devices has led to countless location-based services in which huge amounts of spatio-temporal data get created everyday. Several applications requie efficient processing of queries on the locations of moving objects over time, i.e., the moving object trajectories. This calls for efficient trajectory-based indexing methods that capture both the spatial and temporal dimensions of the data in a way that minimizes the number of disk I/Os required for both updating and querying. Motivated by applications that require only the recent history of a moving objects trajectory, this paper introduces the trails-tree; a disk-based data structure for indexing recent trajectories. The trails-tree maintains a temporal-sliding window over the trajectories and uses: (1) an in-memory memo structure that reduces the I/O cost of updates using a lazy-update mechanism, and (2) a lazy vacuum-cleaning mechanism to delete parts of the trajectories that fall out of the sliding window. Experimental evaluation illustrates that the trails-tree outperforms the state-of-the-art index structures for indexing recent trajectory data by up to a factor of two.

Disk-Based Indexing of Recent Trajectories

The plethora of location-aware devices has led to countless location-based services in which huge amounts of spatiotemporal data get created every day. Several applications require efficient processing of queries on the locations of moving objects over time, i.e., the moving object trajectories. This calls for efficient trajectory-based indexing methods that capture both the spatial and temporal dimensions of the data in a way that minimizes the number of disk I/Os required for both updating and querying. Most existing spatiotemporal index structures capture either the current locations of the moving objects or the entire history of the moving objects. Historical spatiotemporal indexing methods require multiple disk I/Os to process new updates and use a discrete trajectory representation that may result in incomplete query results. In this article, we introduce the trails-tree, a disk-based data structure for indexing recent trajectories. The trails-tree requires half the number of disk I/Os needed by other historical spatiotemporal indexing methods for the insertion and querying operations. We give a detailed description of the trails-tree, and we mathematically analyze its performance. Moreover, we present a novel query processing algorithm that ensures the completeness of the query result set. We experimentally verify the performance of the trails-tree using various real and synthetic datasets.

Cruncher: Distributed in-memory processing for location-based services

Advances in location-based services (LBS) demand high-throughput processing of both static and streaming data. Recently, many systems have been introduced to support distributed main-memory processing to maximize the query throughput. However, these systems are not optimized for spatial data processing. In this demonstration, we showcase Cruncher, a distributed main-memory spatial data warehouse and streaming system. Cruncher extends Spark with adaptive query processing techniques for spatial data. Cruncher uses dynamic batch processing to distribute the queries and the data streams over commodity hardware according to an adaptive partitioning scheme. The batching technique also groups and orders the overlapping spatial queries to enable inter-query optimization. Both the data streams and the offline data share the same partitioning strategy that allows for data co-locality optimization. Furthermore, Cruncher uses an adaptive caching strategy to maintain the frequently-used location data in main memory. Cruncher maintains operational statistics to optimize query processing, data partitioning, and caching at runtime. We demonstrate two LBS applications over Cruncher using real datasets from OpenStreetMap and two synthetic data streams. We demonstrate that Cruncher achieves order(s) of magnitude throughput improvement over Spark when processing spatial data.

Spatio-temporal access methods: a survey (2010 - 2017)

The volume of spatio-temporal data is growing at a rapid pace due to advances in location-aware devices, e.g., smartphones, and the popularity of location-based services, e.g., navigation services. A number of spatio-temporal access methods have been proposed to support efficient processing of queries over the spatio-temporal data. Spatio-temporal access methods can be classified according to the type of data being indexed into the following categories: (1) indexes for historical spatio-temporal data, (2) indexes for current and recent spatio-temporal data, (3) indexes for future spatio-temporal data, (4) indexes for past, present, and future spatio-temporal data, (5) indexes for spatio-temporal data with associated textual data, and (6) parallel and distributed spatio-temporal systems and indexes. This survey is Part 3 of our previous surveys on the same subject (Mokbel et al. IEEE Data Eng Bull 26(2):40–49, 2003; Nguyen-Dinh et al. IEEE Data Eng Bull 33(2):46–55, 2010). In this survey, we present an overview and a broad classification of the spatio-temporal access methods published between 2010 and 2017.