M. Karpovsky, L. Levitin, and A. Trachtenberg. Data verification and reconciliation with generalized error-control codes. In 39th Annual Allerton Conference on Communication, Control, and Computing, 2001.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....to the index. We note that there are a number of traditional approaches to such problems that rely on logs of previous updates or appropriate time stamps. We are currently studying the use of recently studied set reconciliation techniques to solve this problem without logs or time stamps; see [26, 35] for recent work and [46] for a survey. 4 Efficient Query Processing in ODISSEA In this section we describe query processing in the proposed system. A naive implementation of ranked queries with a global index structure would result in transfers of many megabytes of data for many queries from a ....

M. Karpovsky, L. Levitin, and A. Trachtenberg. Data verification and reconciliation with generalized error-control codes. In 39th Annual Allerton Conf. on Communication, Control, and Computing, 2001.

....we can then synchronize them using either the remote file synchronization techniques presented earlier, or by transmitting the entire item in the case of small database records. In the following, we discuss the problems arising in this scenario, with emphasis on a recent approach described in [26, 33, 46]. Some earlier work on reconciliation of record based data appeared in [7, 1, 31, 32] Consider the case, assumed in [46] of a handheld device using the Palm Hotsync program to synchronize its database of addresses or appointments with a desktop device. If the handheld was last synchronized with ....

M. Karpovsky, L. Levitin, and A. Trachtenberg. Data verification and reconciliation with generalized error-control codes. In 39th Annual Allerton Conf. on Communication, Control, and Computing, 2001.

....a file even in the event that every source node with the complete file has left the network. The second challenge of a multi source download algorithm is to make the reconciliation phase as bandwidth efficient as possible. This phase is an instance of the more general set reconciliation problem [10, 4, 11, 2]. Unfortunately, existing set reconciliation algorithms are not practical for multi source download algorithms. They are either too computationally costly, suboptimal in terms of message complexity, or simply too complicated to implement. In this paper, we propose an algorithm that combines ....

M. Karpovsky, L. Levitin, and A. Trachtenberg. Data verification and reconciliation with generalized error-control codes. In 39th Annual Allerton Conference on Communication, Control, and Computing, 2001.

....between two sets result in bitwise errors in the corresponding bit string representations. These bitstrings can then be reconciled with ordinary error correcting codes using techniques described originally in Orlitsky s PhD thesis [46] for linear codes, in [1] for Reed Solomon codes, and in [31] for non linear codes. Unfortunately, the computational complexity of the standard encoding and decoding algorithms for traditional error correcting codes depends computationally on the size of a codeword, which is exponential in b. The approach described above can nonetheless be used to design ....

....3.2 allows us to correct an arbitrary number of errors with an arbitrarily low probability of error. On the other hand, classical implementations of BCH codes correct only a fixed number of errors. Our set reconciliation algorithms are related to BCH codes in general and to RS codes in particular [31] in another way. Specfically, the mathematics of our approach is closely related to the mathematics of BCH and RS decoding. Protocol 3.2 interpolates a rational function which is then factored to find the di#erence sets, whereas in BCH decoding, an error locator polynomial is interpolated and then ....

M. Karpovsky, L. Levitin, and A. Trachtenberg. Data verification and reconciliation with generalized error-control codes. IEEE Trans. on Info. Theory, 2001. submitted.

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M. Karpovsky, L. Levitin, and A. Trachtenberg, "Data verification and reconciliation with generalized error-control codes," 39th Annual Allerton Conference on Communication, Control, and Computing, October 2001.

....of set reconciliation because the structure of data in a file encodes information; for example, a file containing the string a b c is not the same as a file containing the string c b a . The set reconciliation problem is intimately linked to design questions in coding theory and graph theory [10, 11] from which several solutions exist. The following solution, which we have implemented on a PDA as described in Section IV, requires a nearly minimal communication complexity and operates with a reasonable computational complexity. A. Deterministic scheme with a known upper bound The key to the ....

....hosts (i.e. m) is known or tightly bounded. In many practical applications, however, a good bound is not known a priori.The following section describes a probabilistic technique for dealing with such cases. B. Probabilistic scheme with an unknown upper bound An information theoretic analysis [11] shows that if neither a distribution nor a non trivial bound m is known on the differences between two host sets, then no deterministic scheme can do better than slow sync. However, with arbitrarily high probability, a probabilistic scheme can do much better. Specifically, 0 7803 7476 2 02 17.00 ....

M. Karpovsky, L. Levitin, and A. Trachtenberg, "Data verification and reconciliation with generalized error-control codes," IEEE Trans. on Info. Theory, 2001, submitted.

....of set reconciliation because the structure of data in a file encodes information; for example, a file containing the string a b c is not the same as a file containing the string c b a . The set reconciliation problem is intimately linked to design questions in coding theory and graph theory [10, 11] from which several solutions exist. The following solution, which we have implemented on a PDA as described in Section IV, requires a nearly minimal communication complexity and operates with a reasonable computational complexity. A. Deterministic scheme with a known upper bound The key to the ....

M. Karpovsky, L. Levitin, and A. Trachtenberg, "Data verification and reconciliation with generalized error-control codes," 39th Annual Allerton Conference on Communication, Control, and Computing, July 2001.

....of set reconciliation because the structure of data in a file encodes information; for example, a file containing the string a b c is not the same as a file containing the string c b a . The set reconciliation problem is intimately linked to design questions in coding theory and graph theory [10, 11] from which several solutions exist. The following solution, which we have implemented on a PDA as described in Section IV, requires a nearly minimal communication complexity and operates with a reasonable computational complexity. A. Deterministic scheme with a known upper bound The key to the ....

....hosts (i.e. m) is known or tightly bounded. In many practical applications, however, a good bound is not known a priori. The following section describes a probabilistic technique for dealing with such cases. B. Probabilistic scheme with an unknown upper bound An information theoretic analysis [11] shows that if neither a distribution nor a non trivial bound m is known on the differences between two host sets, then no deterministic scheme can do better than slow sync. However, with arbitrarily high probability, a probabilistic scheme can do much better. Specifically, the scheme in [2] ....

M. Karpovsky, L. Levitin, and A. Trachtenberg, "Data verification and reconciliation with generalized error-control codes," IEEE Trans. on Info. Theory, 2001, submitted.

....of set reconciliation because the structure of data in a file encodes information; for example, a file containing the string a b c is not the same as a file containing the string c b a . The set reconciliation problem is intimately linked to design questions in coding theory and graph theory [10, 11] from which several solutions exist. The following solution, which we have implemented on a PDA as described in Section IV, requires a nearly minimal communication complexity and operates with a reasonable computational complexity. A. Deterministic scheme with a known upper bound The key to the ....

M. Karpovsky, L. Levitin, and A. Trachtenberg, "Data verification and reconciliation with generalized error-control codes," 39th Annual Allerton Conference on Communication, Control, and Computing, July 2001.

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M. Karpovsky, L. Levitin, and A. Trachtenberg. Data verification and reconciliation with generalized error-control codes. In 39th Annual Allerton Conference on Communication, Control, and Computing, 2001.

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