Nadav Har'El

Web-a-where: geotagging web content

We describe Web-a-Where, a system for associating geography with Web pages. Web-a-Where locates mentions of places and determines the place each name refers to. In addition, it assigns to each page a geographic focus --- a locality that the page discusses as a whole. The tagging process is simple and fast, aimed to be applied to large collections of Web pages and to facilitate a variety of location-based applications and data analyses.Geotagging involves arbitrating two types of ambiguities: geo/non-geo and geo/geo. A geo/non-geo ambiguity occurs when a place name also has a non-geographic meaning, such as a person name (e.g., Berlin) or a common word (Turkey). Geo/geo ambiguity arises when distinct places have the same name, as in London, England vs. London, Ontario.An implementation of the tagger within the framework of the WebFountain data mining system is described, and evaluated on several corpora of real Web pages. Precision of up to 82% on individual geotags is achieved. We also evaluate the relative contribution of various heuristics the tagger employs, and evaluate the focus-finding algorithm using a corpus pretagged with localities, showing that as many as 91% of the foci reported are correct up to the country level.

ELI: bare-metal performance for I/O virtualization

Direct device assignment enhances the performance of guest virtual machines by allowing them to communicate with I/O devices without host involvement. But even with device assignment, guests are still unable to approach bare-metal performance, because the host intercepts all interrupts, including those interrupts generated by assigned devices to signal to guests the completion of their I/O requests. The host involvement induces multiple unwarranted guest/host context switches, which significantly hamper the performance of I/O intensive workloads. To solve this problem, we present ELI (ExitLess Interrupts), a software-only approach for handling interrupts within guest virtual machines directly and securely. By removing the host from the interrupt handling path, ELI manages to improve the throughput and latency of unmodified, untrusted guests by 1.3x-1.6x, allowing them to reach 97%-100% of bare-metal performance even for the most demanding I/O-intensive workloads.

Beyond basic faceted search

This paper extends traditional faceted search to support richer information discovery tasks over more complex data models. Our first extension adds exible, dynamic business intelligence aggregations to the faceted application, enabling users to gain insight into their data that is far richer than just knowing the quantities of documents belonging to each facet. We see this capability as a step toward bringing OLAP capabilities, traditionally supported by databases over relational data, to the domain of free-text queries over metadata-rich content. Our second extension shows how one can efficiently extend a faceted search engine to support correlated facets - a more complex information model in which the values associated with a document across multiple facets are not independent. We show that by reducing the problem to a recently solved tree-indexing scenario, data with correlated facets can be efficiently indexed and retrieved

Social search and discovery using a unified approach

This research explores new ways to augment the search and discovery of relations between Web 2.0 entities using multiple types and sources of social information. Our goal is to allow the search for all object types such as documents, persons and tags, while retrieving related objects of all types. We implemented a social-search engine using a unified approach, where the search space is expanded to represent heterogeneous information objects that are interrelated by several relation types. Our solution is based on multifaceted search, which provides an efficient update mechanism for relations between objects, as well as efficient search over the heterogeneous data. We describe a social search engine positioned within a large enterprise, applied over social data gathered from several Web 2.0 applications. We conducted a large user study with over 600 people to evaluate the contribution of social data for search. Our results demonstrate the high precision of social search results and confirm the strong relationship of users and tags to the topics retrieved.

Social networks and discovery in the enterprise (SaND)

Traditional information discovery methods are based on content: documents, terms, and the relationships between them. In Web 2.0, people come into play as they create documents and tags in many forms. Personalized search, social graphs, content and people recommendation, are some of the tasks that can take advantage of this newly formed ecosystem. The Social Networks and Discovery (SaND) platform is an aggregation tool for information discovery and analysis over social data gathered from Web 2.0 applications in the enterprise. It leverages complex relationships between content and people as surfaced through the social applications to unleash the value of information. Its integrated index supports combining content-based analysis and people-based analysis over a rich data foundation. Enterprise social data is easily modeled and ingested into SaND, and can be further combined with data from external social applications. This demo will present three main functions provided by SaND: Social search: SaND supports search over the social data using a unified approach [1] in which all system entities (documents, people, tags) are searchable and retrievable (See Figure 1). The search UI enables the searcher to get a wider view on the query topic through results from all entity types, while uncovering the relationships between the on-screen entities. Entity recommendation: SaND can be utilized to recommend people and content for the searcher (Figure 2 shows the “Do You Know” widget for people recommendation). People are recommended according to their “social similarity” relations with the searcher, e.g. organizational and friending ties, similar tagging activity and more. Similarly, content that is related to people that are “socially related” to the searcher is recommended as valuable interesting data. Personalization: Search results are personalized by considering the relations of retrieved entities with the searcher. Entities are ranked according to their relevance to the query as well as according to their relationship strength with the searcher.

Towards exitless and efficient paravirtual I/O

Virtualization is a prominent technology used in data centers around the world. While many kinds of workloads can run at near-native performance even when virtualized, I/O intensive workloads still suffer from high overhead precluding the use of virtualization in many applications. In this paper we tackle the problem of improving the performance of paravirtual I/O. We propose an exitless paravirtual I/O model, under which guests and the hypervisor, running on distinct cores, exchange exitless notifications instead of costly exit-based notifications. Our initial proof of concept improved throughput by 45% and latency by 25μsec compared to a traditional network paravirtual I/O model. We show that a single hypervisor I/O core can become saturated when serving multiple I/O intensive guests, and further research is required to improve scalability in this scenario.

Secure Logical Isolation for Multi-tenancy in cloud storage

Storage cloud systems achieve economies of scale by serving multiple tenants from a shared pool of servers and disks. This leads to the commingling of data from different tenants on the same devices. Typically, a request is processed by an application running with sufficient privileges to access any tenants data; this application authenticates the user and authorizes the request prior to carrying it out. Since the only protection is at the application level, a single vulnerability threatens the data of all tenants, and could lead to cross-tenant data leakage, making the cloud much less secure than dedicated physical resources. To provide security close to physical isolation while allowing complete resource pooling, we propose Secure Logical Isolation for Multi-tenancy (SLIM). SLIM incorporates the first complete security model and set of principles for the safe logical isolation between tenant resources in a cloud storage system, as well as a set of mechanisms for implementing the model. We show how to implement SLIM for OpenStack Swift and present initial performance results.

Bare-metal performance for virtual machines with exitless interrupts

Direct device assignment enhances the performance of guest virtual machines by allowing them to communicate with I/O devices without host involvement. But even with device assignment, guests are still unable to approach bare-metal performance, because the host intercepts all interrupts, including those generated by assigned devices to signal to guests the completion of their I/O requests. The host involvement induces multiple unwarranted guest/host context switches, which significantly hamper the performance of I/O intensive workloads. To solve this problem, we present ExitLess Interrupts (ELI), a software-only approach for handling interrupts within guest virtual machines directly and securely. By removing the host from the interrupt handling path, ELI improves the throughput and latency of unmodified, untrusted guests by 1.3×–1.6×, allowing them to reach 97–100% of bare-metal performance even for the most demanding I/O-intensive workloads.