In digital libraries accessing distributed Web-based bibliographic repositories, efficient query processing requires an advanced caching mechanism to reduce the query response time. Unfortunately, standard page-based and tuple-based caching mechanisms designed for conventional databases are not efficient on the Web, where keyword-based querying is often the only way to retrieve data. In this article, we study the problem of semantic caching of Web queries. We develop a caching mechanism for conjunctive Web queries based on “signature files”. We propose two implementation choices. A first algorithm copes with the relation of semantic containment between a query and the corresponding cache items. A second algorithm extends this processing to more complex cases of semantic intersection. We report results of experiments and show how the caching mechanism is realized in the Knowledge Broker system.
Signature File Methods for Semantic Query Caching
| Boris Chidlovskii, Uwe Borghoff |
| LNCS 1513, Proc. of the 2nd European Conf. on Digital Libraries, September, 1998, Greece |
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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