The Experts below are selected from a list of 44430 Experts worldwide ranked by ideXlab platform
Divesh Srivastava - One of the best experts on this subject based on the ideXlab platform.
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text joins for data cleansing and integration in an rdbms
International Conference on Data Engineering, 2003Co-Authors: Luis Gravano, Panagiotis G. Ipeirotis, Nikolaos Koudas, Divesh SrivastavaAbstract:An organization's data records are often noisy because of transcription errors, incomplete information, lack of standard formats for textual data or combinations thereof. A fundamental task in a data cleaning system is matching textual attributes that refer to the same entity (e.g., organization name or address). This matching is effectively performed via the cosine similarity metric from the information retrieval field. For robustness and scalability, these "text joins" are best done inside an RDBMS, which is where the data is likely to reside. Unfortunately, computing an exact answer to a text join can be expensive. We propose an approximate, sampling-based text join Execution Strategy that can be robustly executed in a standard, unmodified RDBMS.
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ICDE - Text joins for data cleansing and integration in an RDBMS
Proceedings 19th International Conference on Data Engineering (Cat. No.03CH37405), 1Co-Authors: Luis Gravano, Panagiotis G. Ipeirotis, Nikolaos Koudas, Divesh SrivastavaAbstract:An organization's data records are often noisy because of transcription errors, incomplete information, lack of standard formats for textual data or combinations thereof. A fundamental task in a data cleaning system is matching textual attributes that refer to the same entity (e.g., organization name or address). This matching is effectively performed via the cosine similarity metric from the information retrieval field. For robustness and scalability, these "text joins" are best done inside an RDBMS, which is where the data is likely to reside. Unfortunately, computing an exact answer to a text join can be expensive. We propose an approximate, sampling-based text join Execution Strategy that can be robustly executed in a standard, unmodified RDBMS.
Luis Gravano - One of the best experts on this subject based on the ideXlab platform.
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text joins for data cleansing and integration in an rdbms
International Conference on Data Engineering, 2003Co-Authors: Luis Gravano, Panagiotis G. Ipeirotis, Nikolaos Koudas, Divesh SrivastavaAbstract:An organization's data records are often noisy because of transcription errors, incomplete information, lack of standard formats for textual data or combinations thereof. A fundamental task in a data cleaning system is matching textual attributes that refer to the same entity (e.g., organization name or address). This matching is effectively performed via the cosine similarity metric from the information retrieval field. For robustness and scalability, these "text joins" are best done inside an RDBMS, which is where the data is likely to reside. Unfortunately, computing an exact answer to a text join can be expensive. We propose an approximate, sampling-based text join Execution Strategy that can be robustly executed in a standard, unmodified RDBMS.
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ICDE - Text joins for data cleansing and integration in an RDBMS
Proceedings 19th International Conference on Data Engineering (Cat. No.03CH37405), 1Co-Authors: Luis Gravano, Panagiotis G. Ipeirotis, Nikolaos Koudas, Divesh SrivastavaAbstract:An organization's data records are often noisy because of transcription errors, incomplete information, lack of standard formats for textual data or combinations thereof. A fundamental task in a data cleaning system is matching textual attributes that refer to the same entity (e.g., organization name or address). This matching is effectively performed via the cosine similarity metric from the information retrieval field. For robustness and scalability, these "text joins" are best done inside an RDBMS, which is where the data is likely to reside. Unfortunately, computing an exact answer to a text join can be expensive. We propose an approximate, sampling-based text join Execution Strategy that can be robustly executed in a standard, unmodified RDBMS.
Sebastian Jaimungal - One of the best experts on this subject based on the ideXlab platform.
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a closed form Execution Strategy to target volume weighted average price
Siam Journal on Financial Mathematics, 2016Co-Authors: Alvaro Cartea, Sebastian JaimungalAbstract:We provide two explicit closed-form optimal Execution strategies to target volume weighted average price (VWAP). We do this under very general assumptions about the stochastic process followed by the volume traded in the market, and, unlike earlier studies, we account for permanent price impact stemming from order-flow of the agent and all other traders. One of the strategies consists of time weighted average price adjusted upward by a fraction of instantaneous order-flow and adjusted downward by the average order-flow that is expected over the remaining life of the Strategy. The other Strategy consists of the Almgren--Chriss Execution Strategy adjusted by the expected volume and net order-flow during the remaining life of the Strategy. We calibrate model parameters to five stocks traded in Nasdaq (FARO, SMH, NTAP, ORCL, INTC) and use simulations to show that the strategies target VWAP very closely and on average outperform the target by between 0.10 and 8 basis points.
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a closed form Execution Strategy to target volume weighted average price
2015Co-Authors: Alvaro Cartea, Sebastian JaimungalAbstract:We provide two explicit closed-form optimal Execution strategies to target VWAP. We do this under very general assumptions about the stochastic process followed by the volume traded in the market, and, unlike earlier studies, we account for permanent price impact stemming from order-flow of the agent and all other traders. One of the strategies consists of TWAP adjusted upward by a fraction of instantaneous order-flow and adjusted downward by the average order-flow that is expected over the remaining life of the Strategy. The other Strategy consists of the Almgren-Chriss Execution Strategy adjusted by the expected volume and net order-flow during the remaining life of the Strategy. We calibrate model parameters to five stocks traded in Nasdaq (FARO, SMH, NTAP, ORCL, INTC) and use simulations to show that the strategies target VWAP very closely and on average outperform the target by between 0.10 and 8 basis points.
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Optimal Execution with Limit and Market Orders
SSRN Electronic Journal, 2014Co-Authors: Alvaro Cartea, Sebastian JaimungalAbstract:We develop an optimal Execution policy for an investor seeking to execute a large order using limit and market orders. The investor solves the optimal policy considering different restrictions on volume of both types of orders and depth at which limit orders are posted. We show how the Execution policies perform when targeting the volume schedule of the Almgren-Chriss Execution Strategy. The different strategies considered by the investor outperform the Almgren-Chriss price with an average savings per share of about one to two and a half times the spread. This improvement over Almgren-Chriss is due to the strategies benefiting from the optimal mix of limit orders, which earn the spread, and market orders, which keep the investor's inventory schedule on target.
Matthew O. Ward - One of the best experts on this subject based on the ideXlab platform.
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Shared Execution Strategy for neighbor-based pattern mining requests over streaming windows
ACM Transactions on Database Systems, 2012Co-Authors: Di Yang, Elke A. Rundensteiner, Matthew O. WardAbstract:In diverse applications ranging from stock trading to traffic monitoring, data streams are continuously monitored by multiple analysts for extracting patterns of interest in real time. These analysts often submit similar pattern mining requests yet customized with different parameter settings. In this work, we present shared Execution strategies for processing a large number of neighbor-based pattern mining requests of the same type yet with arbitrary parameter settings. Such neighbor-based pattern mining requests cover a broad range of popular mining query types, including detection of clusters, outliers, and nearest neighbors. Given the high algorithmic complexity of the mining process, serving multiple such queries in a single system is extremely resource intensive. The naive method of detecting and maintaining patterns for different queries independently is often infeasible in practice, as its demands on system resources increase dramatically with the cardinality of the query workload. In order to maximize the efficiency of the system resource utilization for executing multiple queries simultaneously, we analyze the commonalities of the neighbor-based pattern mining queries, and identify several general optimization principles which lead to significant system resource sharing among multiple queries. In particular, as a preliminary sharing effort, we observe that the computation needed for the range query searches (the process of searching the neighbors for each object) can be shared among multiple queries and thus saves the CPU consumption. Then we analyze the interrelations between the patterns identified by queries with different parameters settings, including both pattern-specific and window-specific parameters. For that, we first introduce an incremental pattern representation, which represents the patterns identified by queries with different pattern-specific parameters within a single compact structure. This enables integrated pattern maintenance for multiple queries. Second, by leveraging the potential overlaps among sliding windows, we propose a metaquery Strategy which utilizes a single query to answer multiple queries with different window-specific parameters. By combining these three techniques, namely the range query search sharing, integrated pattern maintenance, and metaquery Strategy, our framework realizes fully shared Execution of multiple queries with arbitrary parameter settings. It achieves significant savings of computational and memory resources due to shared Execution. Our comprehensive experimental study, using real data streams from domains of stock trades and moving object monitoring, demonstrates that our solution is significantly faster than the independent Execution Strategy, while using only a small portion of memory space compared to the independent Execution. We also show that our solution scales in handling large numbers of queries in the order of hundreds or even thousands under high input data rates.
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A shared Execution Strategy for multiple pattern mining requests over streaming data
Proceedings of the VLDB Endowment, 2009Co-Authors: Di Yang, Elke A. Rundensteiner, Matthew O. WardAbstract:In diverse applications ranging from stock trading to traffic monitoring, popular data streams are typically monitored by multiple analysts for patterns of interest. These analysts may submit similar pattern mining requests, such as cluster detection queries, yet customized with different parameter settings. In this work, we present an efficient shared Execution Strategy for processing a large number of density-based cluster detection queries with arbitrary parameter settings. Given the high algorithmic complexity of the clustering process and the real-time responsiveness required by streaming applications, serving multiple such queries in a single system is extremely resource intensive. The naive method of detecting and maintaining clusters for different queries independently is often in-feasible in practice, as its demands on system resources increase dramatically with the cardinality of the query workload. To overcome this, we analyze the interrelations between the cluster sets identified by queries with different parameters settings, including both pattern-specific and window-specific parameters. We introduce the notion of the growth property among the cluster sets identified by different queries, and characterize the conditions under which it holds. By exploiting this growth property we propose a uniform solution, called Chandi, which represents identified cluster sets as one single compact structure and performs integrated maintenance on them -- resulting in significant sharing of computational and memory resources. Our comprehensive experimental study, using real data streams from domains of stock trades and moving object monitoring, demonstrates that Chandi is on average four times faster than the best alternative methods, while using 85% less memory space in our test cases. It also shows that Chandi scales in handling large numbers of queries on the order of hundreds or even thousands under high input data rates.
Nikolaos Koudas - One of the best experts on this subject based on the ideXlab platform.
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text joins for data cleansing and integration in an rdbms
International Conference on Data Engineering, 2003Co-Authors: Luis Gravano, Panagiotis G. Ipeirotis, Nikolaos Koudas, Divesh SrivastavaAbstract:An organization's data records are often noisy because of transcription errors, incomplete information, lack of standard formats for textual data or combinations thereof. A fundamental task in a data cleaning system is matching textual attributes that refer to the same entity (e.g., organization name or address). This matching is effectively performed via the cosine similarity metric from the information retrieval field. For robustness and scalability, these "text joins" are best done inside an RDBMS, which is where the data is likely to reside. Unfortunately, computing an exact answer to a text join can be expensive. We propose an approximate, sampling-based text join Execution Strategy that can be robustly executed in a standard, unmodified RDBMS.
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ICDE - Text joins for data cleansing and integration in an RDBMS
Proceedings 19th International Conference on Data Engineering (Cat. No.03CH37405), 1Co-Authors: Luis Gravano, Panagiotis G. Ipeirotis, Nikolaos Koudas, Divesh SrivastavaAbstract:An organization's data records are often noisy because of transcription errors, incomplete information, lack of standard formats for textual data or combinations thereof. A fundamental task in a data cleaning system is matching textual attributes that refer to the same entity (e.g., organization name or address). This matching is effectively performed via the cosine similarity metric from the information retrieval field. For robustness and scalability, these "text joins" are best done inside an RDBMS, which is where the data is likely to reside. Unfortunately, computing an exact answer to a text join can be expensive. We propose an approximate, sampling-based text join Execution Strategy that can be robustly executed in a standard, unmodified RDBMS.