Ranked Spatial-keyword Search over Web-accessible Geotagged Data: State of the Art

Search engines, such as Google and Yahoo!, provide efficient retrieval and ranking of web pages based on queries consisting of a set of given keywords. Recent studies show that 20% of all Web queries also have location constraints, i.e., also refer to the location of a geotagged web page. An increasing number of applications support location based keyword search, including Google Maps, Bing Maps, Yahoo! Local, and Yelp. Such applications depict points of interest on the map and combine their location with the keywords provided by the associated document(s). The posed queries consist of two conditions: a set of keywords and a spatial location. The goal is to find points of interest with these keywords close to the location. We refer to such a query as spatial-keyword query. Moreover, mobile devices nowadays are enhanced with built-in GPS receivers, which permits applications (such as search engines or yellow page services) to acquire the location of the user implicitly, and provide location-based services. For instance, Google Mobile App provides a simple search service for smartphones where the location of the user is automatically captured and employed to retrieve results relevant to her current location. As an example, a search for ”pizza” results in a list of pizza restaurants nearby the user. Given the popularity of spatial-keyword queries and their wide applicability in practical scenarios, it is critical to (i) establish mechanisms for efficient processing of spatial-keyword queries, and (ii) support more expressive query formulation by means of novel 1 query types. Although studies on both keyword search and spatial queries do exist, the problem of combining the search capabilities of both simultaneously has received little attention

Processing Data-Intensive Workflows in the Cloud

In the recent years, large-scale data analysis has become critical to the success of modern enterprise. Meanwhile, with the emergence of cloud computing, companies are attracted to move their data analytics tasks to the cloud due to its exible, on demand resources usage and pay-as-you-go pricing model. MapReduce has been widely recognized as an important tool for performing large-scale data analysis in the cloud. It provides a simple and fault-tolerance framework for users to process data-intensive analytics tasks in parallel across dierent physical machines. In this report, we survey alternative implementations of MapReduce, contrasting batched-oriented and pipelined execution models and study how these models impact response times, completion time and robustness. Next, we present three optimization strategies for MapReduce-style work- ows, including (1) scan sharing across MapReduce programs, (2) work- ow optimizations aimed at reducing intermediate data, and (3) schedul- ing policies that map work ow tasks to dierent machines in order to minimize completion times and monetary costs. We conclude with a brief comparison across these optimization strate- gies, and discuss their pros/cons as well as performance implications of using more than one optimization strategy at a time.University of Pennsylvania Department of Computer and Information Science Technical Report No. MS-CIS-12-07

Explainable and Resource-Efficient Stream Processing Through Provenance and Scheduling

In our era of big data, information is captured at unprecedented volumes and velocities, with technologies such as Cyber-Physical Systems making quick decisions based on the processing of streaming, unbounded datasets. In such scenarios, it can be beneficial to process the data in an online manner, using the stream processing paradigm implemented by Stream Processing Engines (SPEs). While SPEs enable high-throughput, low-latency analysis, they are faced with challenges connected to evolving deployment scenarios, like the increasing use of heterogeneous, resource-constrained edge devices together with cloud resources and the increasing user expectations for usability, control, and resource-efficiency, on par with features provided by traditional databases.This thesis tackles open challenges regarding making stream processing more user-friendly, customizable, and resource-efficient. The first part outlines our work, providing high-level background information, descriptions of the research problems, and our contributions. The second part presents our three state-of-the-art frameworks for explainable data streaming using data provenance, which can help users of streaming queries to identify important data points, explain unexpected behaviors, and aid query understanding and debugging. (A) GeneaLog provides backward provenance allowing users to identify the inputs that contributed to the generation of each output of a streaming query. (B) Ananke is the first framework to provide a duplicate-free graph of live forward provenance, enabling easy bidirectional tracing of input-output relationships in streaming queries and identifying data points that have finished contributing to results. (C) Erebus is the first framework that allows users to define expectations about the results of a streaming query, validating whether these expectations are met or providing explanations in the form of why-not provenance otherwise. The third part presents techniques for execution efficiency through custom scheduling, introducing our state-of-the-art scheduling frameworks that control resource allocation and achieve user-defined performance goals. (D) Haren is an SPE-agnostic user-level scheduler that can efficiently enforce user-defined scheduling policies. (E) Lachesis is a standalone scheduling middleware that requires no changes to SPEs but, instead, directly guides the scheduling decisions of the underlying Operating System. Our extensive evaluations using real-world SPEs and workloads show that our work significantly improves over the state-of-the-art while introducing only small performance overheads

UniStore: Querying a DHT-based Universal Storage

In recent time, the idea of collecting and combining large public data sets and services became more and more popular. The special characteristics of such systems and the requirements of the participants demand for strictly decentralized solutions. However, this comes along with several ambitious challenges a corresponding system has to overcome. In this demonstration paper, we present a light-weight distributed universal storage capable of dealing with those challenges, and providing a powerful and flexible way of building Internet-scale public data management systems. We introduce our approach based on a triple storage on top of a DHT overlay system, based on the ideas of a universal relation model and RDF, outline solved challenges and open issues, and present usage as well as demonstration aspects of the platform