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Argus: Efficient Scalable Continuous Query Optimization For Large-Volume Data Streams, Chun Jin, Jaime G. Carbonell
Argus: Efficient Scalable Continuous Query Optimization For Large-Volume Data Streams, Chun Jin, Jaime G. Carbonell
Jaime G. Carbonell
We present the architecture of ARGUS, a stream processing system implemented atop commercial DBMSs to support large-scale complex continuous queries over data streams. ARGUS supports incremental operator evaluation and incremental multi-query plan optimization as new queries arrive. The latter is done to a degree well beyond the previous state-of-the-art via a suite of techniques such as query-algebra canonicalization, indexing, and searching, and topological query network optimization with join order optimization, conditional materialization, minimal column projection, and transitivity inference. Building on top of a DBMS, the system provides a value-adding package to the existing database applications where the needs of stream …
Document Classification Of Protein Sequences, Betty Yee Man Cheng, Jaime G. Carbonell, Judith Klein-Seetharaman
Document Classification Of Protein Sequences, Betty Yee Man Cheng, Jaime G. Carbonell, Judith Klein-Seetharaman
Jaime G. Carbonell
The need for accurate, automated protein classification methods continues to increase as advances in biotechnology uncovers new proteins at a fast rate. G-protein coupled receptors (GPCRs) are a particularly difficult superfamily of proteins to classify due to the extreme diversity among its members; yet, they are an important subject in pharmacological research being the target of approximately 60% of current drugs (Muller, 2000). A comparison of BLAST, k-NN, HMM and SVM with alignment-based features by Karchin et al. (2002) has suggested that classifiers at the complexity of SVM are needed to attain high accuracy in GPCR subfamily classification. Here, analogous …
Scheduling With Uncertain Resources: Collaboration With The User, Eugene Fink, Ulas Bardak, Brandon Rothrock, Jaime G. Carbonell
Scheduling With Uncertain Resources: Collaboration With The User, Eugene Fink, Ulas Bardak, Brandon Rothrock, Jaime G. Carbonell
Jaime G. Carbonell
We describe a scheduling system that supports collaboration between the user and automated optimizer. It enables the user to monitor the optimizer decisions, make any of the decisions manually, and leave the other decisions to the system. Furthermore, it identifies the tasks that require the user’s participation, and asks for assistance with these tasks.
Learning By Analogical Replay In Prodigy: First Results, Manuela M. Veloso, Jaime G. Carbonell
Learning By Analogical Replay In Prodigy: First Results, Manuela M. Veloso, Jaime G. Carbonell
Jaime G. Carbonell
Robust reasoning requires learning from problem solving episodes. Past experience must be compiled to provide adaptation to new contingencies and intelligent modification of solutions to past problems. This paper presents a comprehensive computational model of analogical reasoning that transitions smoothly between case replay, case adaptation, and general problem solving, exploiting and modifying past experience when available and resorting to general problem-solving methods when required. Learning occurs by accumulation and reuse of cases (problem solving episodes), especially in situations that required extensive problem solving, and by tuning the indexing structure of the memory model to retrieve progressively more appropriate cases. The …
Mt For Minority Languages Using Elicitation-Based Learning Of Syntactic Transfer Rules, Katharina Probst, Lori Levin, Erik Peterson, Alon Lavie, Jaime G. Carbonell
Mt For Minority Languages Using Elicitation-Based Learning Of Syntactic Transfer Rules, Katharina Probst, Lori Levin, Erik Peterson, Alon Lavie, Jaime G. Carbonell
Jaime G. Carbonell
The AVENUE project contains a run-time machine translation program that is surrounded by pre- and post-run-time modules. The post-run-time module selects among translation alternatives. The pre-run-time modules are concerned with elicitation of data and automatic learning of transfer rules in order to facilitate the development of machine translation between a language with extensive resources for natural language processing and a language with few resources for natural language processing. This paper describes the run-time transfer-based machine translation system as well as two of the pre-run-time modules: elicitation of data from the minority language and automated learning of transfer rules from the …