21367461
A feasible method to find areas with constraints using hierarchical depth-first clustering
Addresses a reliable, feasible method to find geographical areas with constraints using hierarchical depth-first clustering. The method involves multi-level hierarchical clustering with a depth-first strategy, depending on whether the area of each cluster satisfies the given constraints. The attributes used in the hierarchical clustering are the coordinates of the grid data points. The constraints are an average value range and the minimum size of an area with a small proportion of missing data points. Convex-hull and point-in-polygon algorithms are involved in examining the constraint satisfaction. The method is implemented for an Earth science data set for vegetation studies - the Normalized Difference Vegetation Index (NVDI).
3129932
An efficient method to find area clusters with constraints using grid index structure
This work presents an efficient method to find area clusters satisfying certain constraints. The method uses a grid index structure to find them by examining five cell, 8-connected neighborhood. The hierarchical clustering technique is applied for preprocessing the grid with similar value distribution into the same clusters. This preprocessing helps reduce the computational time and eliminate the possible outlier grid cells. The constraints are average value range, minimum area size and minimum missing data ratio. The method is implemented for a remote sensing data, MOD08/spl I.bar/M3, which is MODIS (MODerate resolution Imaging Spectroradiometer) level 3 monthly atmospheric product.
40685825
Value Range Queries on Earth Science Data via Histogram Clustering
Remote sensing data as well as ground-based and model output data about the Earth system can be very large in volume. On the other hand, in order to use the data efficiently, scientists need to search for data based on not only metadata but also actual data values. To answer value range queries by scanning very large volumes of data is obviously unrealistic. This article studies a clustering technique on histograms of data values on predefined cells to index the cells. Through this index system, the so-called statistical range queries can be answered quickly and approximately together with an accuracy assessment. Examples of using this technique for Earth science data sets are given in this article.
26832202
Approximate search engine optimization for directory service
Today, in many practical e-commerce systems, the real stored data usually are short strings, such as names, addresses, or other information. Searching data within these short strings is not the same as searching within longer strings. General search engines try their best to scan all long strings (or articles) quickly, and find out the places that match the search conditions. Some great online search algorithms (such as "agrep" as used inside glimpse, or "cgrep " as used inside compressed indices, or 'NR-grep') are proposed for searching without any indices in the sub-linear time O(n). However, for short strings (n is small), the practical performance of algorithms of O(n) and O(n) are much the same. Therefore, suitable indices are necessary to optimize the performance of the search engine. On the other hand, directory services are more and more important because of its optimization for searching data. The data stored in directory servers are almost short strings. The approximate search engine for directory service must take the properties of short strings into considerations. In our previous research, we have designed one approximate search engine especially for short strings by using filters to filter out the possible short strings, and then checking for the answers. However the performance of the previous search engine needs to be enhanced. In this paper, we propose new architecture and algorithm to optimize the performance of searching for directory service.
454393
Private Keyword-Based Push and Pull with Applications to Anonymous Communication
We propose a new keyword-based Private Information Retrieval (PIR) model that allows private modification of the database from which information is requested. In our model, the database is distributed over n servers, any one of which can act as a transparent interface for clients. We present protocols that support operations for accessing data, focusing on privately appending labelled records to the database (push) and privately retrieving the next unseen record appended under a given label (pull). The communication complexity between the client and servers is independent of the number of records in the database (or more generally, the number of previous push and pull operations) and of the number of servers. Our scheme also supports access control oblivious to the database servers by implicitly including a public key in each push, so that only the party holding the private key can retrieve the record via pull. To our knowledge, this is the first system that achieves the following properties: private database modification, private retrieval of multiple records with the same keyword, and oblivious access control. We also provide a number of extensions to our protocols and, as a demonstrative application, an unlinkable anonymous communication service using them.
7799230
Passage feedback with IRIS
We compare a user-defined passage feedback (pf) system to a document feedback (df) system. Df employed the adaptive linear model for retrieval, while pf used weighted query expansion based on positive and negative feedback. Twenty-four searchers performed the same six tasks in varying search and system-order per TREC-8 guidelines. We hypothesized that pf, which featured interactive query expansion, would outperform df, which relied on automatic query expansion. Initial analysis appeared to reject this hypothesis, as df showed slightly higher overall performance than pf. However, analysis by system-order groups indicates only the first pf use had lower performance. These data suggest that pf was more difficult to learn than df, though the second pf use yielded competitive performance. If performance of pf is indeed affected by learning, an improved pf system with usability enhancements may prove to be an effective mechanism for interactive information retrieval.
6906500
Web Appearance Disambiguation of Personal Names Based on Network Motif
Searching for information about a particular person is a common activity on search engines. However, current search engines do not provide any special function for search a person. Previous research has solved the problem by using additional background knowledge, such as a friend list, to cluster the searched Web pages. However, it is still difficult to retrieve and choose suitable background knowledge. In this paper, we propose a Web appearance disambiguation (WAD) system to solve the problem by only using the hyperlink structures between Web pages. The key idea of the WAD system is to find out smaller node motifs as evidences of close relationship between pages for clustering searched Web pages. Our experimental results show that, under no background knowledge, the performance of the WAD system achieves 70% for the F-measure
10296067
IRIS at TREC-8
We tested two relevance feedback models, an adaptive linear model and a probabilistic model, using massive feedback query expansion in TREC-5 (Sumner & Shaw, 1997), experimented with a three-valued scale of relevance and reduced feedback query expansion in TREC-6 (Sumner, Yang, Akers & Shaw, 1998), and examined the effectiveness of relevance feedback using a subcollection and the effect of system features in an interactive retrieval system called IRIS (Information Retrieval Interactive System) in TREC-7 (Yang, Maglaughlin, Mehol & Sumner, 1999). In TREC-8, we continued our exploration of relevance feedback approaches. Based on the result of our TREC-7 interactive experiment, which suggested relevance feedback using user-selected passages to be an effective alternative to conventional document feedback, our TREC-8 interactive experiment compared a passage feedback system and a document feedback system that were identical in all aspects except for the feedback mechanism. For the TREC-8 ad-hoc task, we merged results of pseudo-relevance feedback to subcollections as in TREC-7. Our results were consistent with that of TREC-7. The results of passage feedback, whose system log showed high level of searcher intervention, was superior to the document feedback results. As in TREC-7, our ad-hoc results showed high precision in top few documents, but performed poorly overall compared to results using the collection as a whole.