AT&T Labs

About: AT&T Labs is a based out in . It is known for research contribution in the topics: Network packet & The Internet. The organization has 1879 authors who have published 5595 publications receiving 483151 citations.

Privacy awareness about information leakage: who knows what about me?

Abstract: The task of protecting users' privacy is made more difficult by their attitudes towards information disclosure without full awareness and the economics of the tracking and advertising industry. Even after numerous press reports and widespread disclosure of leakages on the Web and on popular Online Social Networks, many users appear not be fully aware of the fact that their information may be collected, aggregated and linked with ambient information for a variety of purposes. Past attempts at alleviating this problem have addressed individual aspects of the user's data collection. In this paper we move towards a comprehensive and efficient client-side tool that maximizes users' awareness of the extent of their information leakage. We show that such a customizable tool can help users to make informed decisions on controlling their privacy footprint.

Global distance-based segmentation of trajectories

Abstract: This work introduces distance-based criteria for segmentation of object trajectories. Segmentation leads to simplification of the original objects into smaller, less complex primitives that are better suited for storage and retrieval purposes. Previous work on trajectory segmentation attacked the problem locally, segmenting separately each trajectory of the database. Therefore, they did not directly optimize the inter-object separability, which is necessary for mining operations such as searching, clustering, and classification on large databases. In this paper we analyze the trajectory segmentation problem from a global perspective, utilizing data aware distance-based optimization techniques, which optimize pairwise distance estimates hence leading to more efficient object pruning. We first derive exact solutions of the distance-based formulation. Due to the intractable complexity of the exact solution, we present anapproximate, greedy solution that exploits forward searching of locally optimal solutions. Since the greedy solution also imposes a prohibitive computational cost, we also put forward more light weight variance-based segmentation techniques, which intelligently "relax" the pairwise distance only in the areas that affect the least the mining operation.

A heartbeat mechanism and its application in gigascope

Abstract: Data stream management systems often rely on ordering properties of tuple attributes in order to implement non-blocking operators. However, query operators that work with multiple streams, such as stream merge or join, can often still block if one of the input stream is very slow or bursty. In principle, punctuation and heartbeat mechanisms have been proposed to unblock streaming operators. In practice, it is a challenge to incorporate such mechanisms into a high-performance stream management system that is operational in an industrial application.In this paper, we introduce a system for punctuation-carrying heartbeat generation that we developed for Gigascope, a high-performance streaming database for network monitoring, that is operationally used within AT&T's IP backbone. We show how heartbeats can be regularly generated by low-level nodes in query execution plans and propagated upward unblocking all streaming operators on its way. Additionally, our heartbeat mechanism can be used for other applications in distributed settings such as detecting node failures, performance monitoring, and query optimization. A performance evaluation using live data feeds shows that our system is capable of working at multiple Gigabit line speeds in a live, industrial deployment and can significantly decrease the query memory utilization.

A comprehensive solution to the XML-to-relational mapping problem

Abstract: The use of relational database management systems (RDBMSs) to store and query XML data has attracted considerable interest with a view to leveraging their powerful and reliable data management services. Due to the mismatch between the relational and XML data models, it is necessary to first shred and load the XML data into relational tables, and then btranslate XML queries over the original data into equivalent SQL queries over the mapped tables. Although there is a rich literature on XML-relational storage, none of the existing solutions addresses all the storage problems in a single framework. Works on mapping strategies often have little or no details about query translation, and proposals for query translation often target a specific mapping strategy. XML-storage solutions provided by RDBMS also have limitations. Notably, they are tied to a specific backend and use proprietary mapping languages, which not only may require a steep learning curve, but often are unable to express certain desirable mappings.In order to address these limitations, we developed ShreX, a XML-to-relational mapping framework and system that provides the first comprehensive and end-to-end solution to the relational storage of XML data. Mappings in ShreX are defined through annotations to an XML Schema. The use of XML Schema simplifies the mapping process, since it does not require users to master a new specialized mapping language. The use of annotations allows mapping choices to be combined in many different ways. As a result, ShreX not only supports all the mapping strategies proposed in the literature, but also new useful strategies that had not been considered previously. ShreX provides generic (and automatic) document shredding and query translation capabilities; and it is portable --- its mapping specifications are independent of the database backend.