Ives, Zachary
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Disciplines
Computer Sciences
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Position
Assistant Professor
Introduction
Zachary Ives is an Assistant Professor at the University of Pennsylvania and an Associated Faculty Member of the Penn Center for Bioinformatics. He received his B.S. from Sonoma State University and his PhD from the University of Washington. His research interests include data integration, peer-to-peer models of data sharing, processing and security of heterogeneous sensor streams, and data exchange between autonomous systems. He is a recipient of the NSF CAREER award and a member of the DARPA Computer Science Study Panel.
Research Interests
Databases, data integration, peer-to-peer computing, sensor networks
Collection
43 results
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Now showing 1 - 10 of 43
Publication MOSAIC: Multiple Overlay Selection and Intelligent Composition(2007-10-24) Loo, Boon Thau; Ives, Zachary G; Mao, Yun; Smith, Jonathan MToday, the most effective mechanism for remedying shortcomings of the Internet, or augmenting it with new networking capabilities, is to develop and deploy a new overlay network. This leads to the problem of multiple networking infrastructures, each with independent advantages, and each developed in isolation. A greatly preferable solution is to have a single infrastructure under which new overlays can be developed, deployed, selected, and combined according to application and administrator needs. MOSAIC is an extensible infrastructure that enables not only the specification of new overlay networks, but also dynamic selection and composition of such overlays. MOSAIC provides declarative networking: it uses a unified declarative language (Mozlog) and runtime system to enable specification of new overlay networks, as well as their composition in both the control and data planes. Importantly, it permits dynamic compositions with both existing overlay networks and legacy applications. This paper demonstrates the dynamic selection and composition capabilities of MOSAIC with a variety of declarative overlays: an indirection overlay that supports mobility (i3), a resilient overlay (RON), and a transport-layer proxy. Using a remarkably concise specification, MOSAIC provides the benefits of runtime composition to simultaneously deliver application-aware mobility, NAT traversal and reliability with low performance overhead, demonstrated with deployment and measurement on both a local cluster and the PlanetLab testbed.Publication Sideways Information Passing for Push-Style Query Processing(2007-11-20) Ives, Zachary G; Taylor, Nicholas EIn many modern data management settings, data is queried from a central node or nodes, but is stored at remote sources. In such a setting it is common to perform "push-style" query processing, using multithreaded pipelined hash joins and bushy query plans to compute parts of the query in parallel; to avoid idling, the CPU can switch between them as delays are encountered. This works well for simple select-project-join queries, but increasingly, Web and integration applications require more complex queries with multiple joins and even nested subqueries. As we demonstrate in this paper, push-style execution of complex queries can be improved substantially via sideways information passing; push-style queries provide many opportunities for information passing that have not been studied in the past literature. We present adaptive information passing, a general runtime decisionmaking technique for reusing intermediate state from one query subresult to prune and reduce computation of other subresults. We develop two alternative schemes for performing adaptive information passing, which we study in several settings under a variety of workloads.Publication Update Exchange With Mappings and Provenance(2007-11-27) Green, Todd J; Karvounarakis, Grigoris; Ives, Zachary G; Tannen, ValWe consider systems for data sharing among heterogeneous peers related by a network of schema mappings. Each peer has a locally controlled and edited database instance, but wants to ask queries over related data from other peers as well. To achieve this, every peer’s updates propagate along the mappings to the other peers. However, this update exchange is filtered by trust conditions — expressing what data and sources a peer judges to be authoritative — which may cause a peer to reject another’s updates. In order to support such filtering, updates carry provenance information. These systems target scientific data sharing applications, and their general principles and architecture have been described in [21]. In this paper we present methods for realizing such systems. Specifically, we extend techniques from data integration, data exchange, and incremental view maintenance to propagate updates along mappings; we integrate a novel model for tracking data provenance, such that curators may filter updates based on trust conditions over this provenance; we discuss strategies for implementing our techniques in conjunction with an RDBMS; and we experimentally demonstrate the viability of our techniques in the Orchestra prototype system. This technical report supersedes the version which appeared in VLDB 2007 [17] and corrects certain technical claims regarding the semantics of our system (see errata in Sections [3.1] and [4.1.1]).Publication Interviewing During a Tight Job Market(2002-09-01) Ives, Zachary G; Luo, QiongVarious tips for interviewing for PhD graduates, seeking an academic position in a research university in Asia or North America are discussed. It is suggested that having the dissertation done before interviews gives a large degree of relief on one's mind. It is found that to be practical about job research package and keep a close eye on applications increases the confidence level. It is also observed that the questions during the talk provides opportunity to clarify and strengthen the talk and show this ability during the interview.Publication Provenance in ORCHESTRA(2010-01-01) Green, Todd J; Ives, Zachary G; Karvounarakis, Grigoris; Tannen, ValSharing structured data today requires agreeing on a standard schema, then mapping and cleaning all of the data to achieve a single queriable mediated instance. However, for settings in which structured data is collaboratively authored by a large community, such as in the sciences, there is seldom con- sensus about how the data should be represented, what is correct, and which sources are authoritative. Moreover, such data is dynamic: it is frequently updated, cleaned, and annotated. The ORCHESTRA collaborative data sharing system develops a new architecture and consistency model for such settings, based on the needs of data sharing in the life sciences. A key aspect of ORCHESTRA’s design is that the provenance of data is recorded at every step. In this paper we describe ORCHESTRA’s provenance model and architecture, emphasizing its integral use of provenance in enforcing trust policies and translating updates efficiently.Publication Dynamic Join Optimization in Multi-Hop Wireless Sensor Networks(2010-01-01) Mihaylov, Svilen; Ives, Zachary G; Jacob, Marie; Guha, SudiptoTo enable smart environments and self-tuning data centers, we are developing the Aspen system for integrating physical sensor data, as well as stream data coming from machine logical state, and database or Web data from the Internet. A key component of this system is a query processor optimized for limited-bandwidth, possibly battery-powered devices with multiple hop wireless radio communications. This query processor is given a portion of a data integration query, possibly including joins among sensors, to execute. Several recent papers have developed techniques for computing joins in sensors, but these techniques are static and are only appropriate for specific join selectivity ratios. We consider the problem of dynamic join optimization for sensor networks, developing solutions that employ cost modeling, as well as adaptive learning and self-tuning heuristics to choose the best algorithm under real and variable selectivity values. We focus on in-network join computation, but our architecture extends to other approaches (and we compare against these). We develop basic techniques assuming selectivities are uniform and known in advance, and optimization can be done on a pairwise basis; we then extend the work to handle joins between multiple pairs, when selectivities are not fully known. We experimentally validate our work at scale using standard datasets.Publication SmartCIS: Integrating Digital and Physical Environments(2010-01-01) Liu, Mengmeng; Mihaylov, Svilen; Ives, Zachary G; Bao, Zhuowei; Loo, Boon Thau; Jacob, Marie; Guha, SudiptoPublication Automatically Incorporating New Sources in Keyword Search-Based Data Integration(2010-06-01) Talukdar, Partha; Ives, Zachary G; Pereira, FernandoScientific data offers some of the most interesting challenges in data integration today. Scientific fields evolve rapidly and accumulate masses of observational and experimental data that needs to be annotated, revised, interlinked, and made available to other scientists. From the perspective of the user, this can be a major headache as the data they seek may initially be spread across many databases in need of integration. Worse, even if users are given a solution that integrates the current state of the source databases, new data sources appear with new data items of interest to the user. Here we build upon recent ideas for creating integrated views over data sources using keyword search techniques, ranked answers, and user feedback [32] to investigate how to automatically discover when a new data source has content relevant to a user’s view — in essence, performing automatic data integration for incoming data sets. The new architecture accommodates a variety of methods to discover related attributes, including label propagation algorithms from the machine learning community [2] and existing schema matchers [11]. The user may provide feedback on the suggested new results, helping the system repair any bad alignments or increase the cost of including a new source that is not useful. We evaluate our approach on actual bioinformatics schemas and data, using state-of-the-art schema matchers as components. We also discuss how our architecture can be adapted to more traditional settings with a mediated schema.Publication Orchestra: Facilitating Collaborative Data Sharing(2007-06-11) Green, Todd J; Karvounarakis, Grigoris; Taylor, Nicholas E; Biton, Olivier; Ives, Zachary G; Tannen, ValOne of the most elusive goals of structured data management has been sharing among large, heterogeneous populations: while data integration [4, 10] and exchange [3] are gradually being adopted by corporations or small confederations, little progress has been made in integrating broader communities. Yet the need for large-scale sharing of heterogeneous data is increasing: most of the sciences, particularly biology and astronomy, have become data-driven as they have attempted to tackle larger questions. The field of bioinformatics, in particular, has seen a plethora of different databases emerge: each is focused on a related but subtly different collection of organisms (e.g., CryptoDB, TIGR, FlyNome), genes (GenBank, GeneDB), proteins (UniProt, RCSB Protein Databank), diseases (OMIM, GeneDis), and so on. Such communities have a pressing need to interlink their heterogeneous databases in order to facilitate scientific discovery.Publication Piazza: Data Management Infrastructure for Semantic Web Applications(2003-05-20) Halevy, Alon Y; Ives, Zachary G; Mork, Peter; Tatarinov, IgorThe Semantic Web envisions a World Wide Web in which data is described with rich semantics and applications can pose complex queries. To this point, researchers have defined new languages for specifying meanings for concepts and developed techniques for reasoning about them, using RDF as the data model. To flourish, the Semantic Web needs to be able to accommodate the huge amounts of existing data and the applications operating on them. To achieve this, we are faced with two problems. First, most of the world's data is available not in RDF but in XML; XML and the applications consuming it rely not only on the domain structure of the data, but also on its document structure. Hence, to provide interoperability between such sources, we must map between both their domain structures and their document structures. Second, data management practitioners often prefer to exchange data through local point-to-point data translations, rather than mapping to common mediated schemas or ontologies. This paper describes the Piazza system, which addresses these challenges. Piazza offers a language for mediating between data sources on the Semantic Web, which maps both the domain structure and document structure. Piazza also enables interoperation of XML data with RDF data that is accompanied by rich OWL ontologies. Mappings in Piazza are provided at a local scale between small sets of nodes, and our query answering algorithm is able to chain sets mappings together to obtain relevant data from across the Piazza network. We also describe an implemented scenario in Piazza and the lessons we learned from it.