Abstract: The present disclosure is directed to systems, methods, and computer-readable storage media for publishing data. A data summary summarizing the data can be generated and published according to several publishing schemes. In some embodiments, non-zero entries are selected and modified and zero entries are sampled according to one or more distribution functions. The sampled and modified values are added to a data summary, or a sample of the sampled and modified values are added to the data summary. The data summary is published, released, used, or otherwise output. In other embodiments, priority values are assigned to each value associated with the data, and a number of entries with the highest values are selected and added to the data summary.

Abstract: Aggregates are calculated from a data stream in which data is sent in a sequence of tuples, in which each tuple comprises an item identifier and a timestamp indicating when the tuple was transmitted. The tuples may arrive at a data receiver out-of-order, that is, the sequence in which the tuples arrive are not necessarily in the same sequence as their corresponding timestamps. In calculating aggregates, more recent data may be given more weight by a decay function which is a function of the timestamp associated with the tuple and the current time. The statistical characteristics of the tuples are summarized by a set of linear data summaries. The set of linear data summaries are generated such that only a single linear data summary falls between a set of boundaries calculated from the decay function and a set of timestamps. Aggregates are calculated from the set of linear data summaries.

Abstract: The present disclosure is directed to systems, methods, and computer-readable storage media for sampling from distributed data streams. Data elements are received at site servers configured to collect and report data to a coordinator device. The site servers assign a binary string to each of the data elements. Each bit of the binary strings can be independently set to a 0 or a 1 with a probability of one half. The binary string is used to sample from the received data elements, and the data elements and/or the sampled data elements can be transmitted to a coordinator device. The coordinator device can examine one or more bits of the binary string to draw samples of the received data elements in accordance with desired probabilities.

Abstract: Methods, apparatus, and articles of manufacture are disclosed to sample signed weighted updates. Example methods disclosed herein include identifying a first key associated with a data update obtained by sampling a stream of data received from a plurality of nodes in a network, the first key being representative of a first node in the plurality of nodes, adjusting a first value associated with the first key based on a weight associated with the data update, the weight being positive when the data update corresponds to opening of a new data connection with the first node, the weight being negative when the data update corresponds to closing of an existing data connection with the first node, and estimating a status of the first node based on the first value.

Abstract: Aggregates are calculated from a data stream in which data is sent in a sequence of tuples, in which each tuple comprises an item identifier and a timestamp indicating when the tuple was transmitted. The tuples may arrive out-of-order, that is, the sequence in which the tuples arrive are not necessarily in the sequence of their corresponding timestamps. In calculating aggregates, more recent data may be given more weight by multiplying each tuple by a decay function which is a function of the timestamp associated with the tuple and the current time. The tuples are recorded in a quantile-digest data structure. Aggregates are calculated from the data stored in the quantile-digest data structure.

Abstract: The present disclosure is directed to systems, methods, and computer-readable storage media for anonymizing data over multiple temporal releases. Data is received, and nodes and connections in the data are identified. The data also is analyzed to identify predicted connections. The nodes, the connections, and the predicted connections are analyzed to determine how to group the nodes in the data. The data is published, and the grouping of the nodes is extended to subsequent temporal releases of the data, the nodes of which are grouped in accordance with the grouping used with the data.

Abstract: Disclosed are method and apparatus for identifying members of a social network who have a high likelihood of providing a useful response to a query. A query engine examines the personal pages of a set of members and automatically gleans semantic information relevant to the query. From the automatically-gleaned semantic information, a score indicative of the likelihood that the member may provide a useful response is calculated.

Abstract: Methods and apparatus for ranking uncertain data in a probabilistic database are disclosed. An example method disclosed herein comprises using a set of data tuples representing a plurality of possible data set instantiations associated with a respective plurality of instantiation probabilities to store non-deterministic data in a database, each data tuple corresponding to a set of possible data tuple instantiations, each data set instantiation realizable by selecting a respective data tuple instantiation for at least some of the data tuples, the method further comprising determining an expected rank for each data tuple included in at least a subset of the set of data tuples, the expected rank for a particular data tuple representing a combination of weighted component ranks of the particular data tuple, each component rank representing a ranking of the data tuple in a corresponding data set instantiation, each component ranking weighted by a respective instantiation probability.

Abstract: A disclosed example method involves generating a plurality of wavelet coefficient quantities. Each wavelet coefficient quantity is generated based on items represented by probabilistic data. Each wavelet coefficient quantity represents different ones of the items by multiplying corresponding wavelet vectors. The example method also involves determining an error measure associated with each of the plurality of wavelet coefficient quantities, and selecting at least one of the plurality of wavelet coefficient quantities based on its associated error measure. The method also involves displaying parameter information associated with the one of the plurality of wavelet coefficient quantities to represent the probabilistic data.

Abstract: Methods, apparatus, and articles of manufacture are disclosed to sample signed weighted updates. Example methods disclosed herein include identifying a first key associated with a data update obtained by sampling a stream of data received from a plurality of nodes in a network, the first key being representative of a first node in the plurality of nodes, adjusting a first value associated with the first key based on a weight associated with the data update, the weight being positive when the data update corresponds to opening of a new data connection with the first node, the weight being negative when the data update corresponds to closing of an existing data connection with the first node, and estimating a status of the first node based on the first value.

Abstract: A method and apparatus for tracking communications in a network are disclosed. For example, the method receives a subscription from a customer for a service to track at least one variable associated with a plurality of communicants of the customer. The method identifies a plurality of members of a social network of the customer, and gathers communication data associated with the plurality of members for tracking the at least one variable. The method then displays at least one result derived from the communication data to the customer.

Abstract: The present disclosure is directed to systems, methods, and computer-readable storage media for generating data and data sets that are resistant to minimality attacks. Data sets having a number of tuples are received, and the tuples are ordered according to an aspect of the tuples. The tuples can be split into groups of tuples, and each of the groups may be analyzed to determine if the group complies with a privacy requirement. Groups that satisfy the privacy requirement may be output as new data sets that are resistant to minimality attacks.

Abstract: A method for validating outsourced processing of a priority queue includes configuring a verifier for independent, single-pass processing of priority queue operations that include insertion operations and extraction operations and priorities associated with each operation. The verifier may be configured to validate N operations using a memory space having a size that is proportional to the square root of N using an algorithm to buffer the operations as a series of R epochs. Extractions associated with each individual epoch may be monitored using arrays Y and Z. Insertions for the epoch k may monitored using arrays X and Z. The processing of the priority queue operations may be verified based on the equality or inequality of the arrays X, Y, and Z. Hashed values for the arrays may be used to test their equality to conserve storage requirements.

Abstract: A disclosed method for implementing time decay in the analysis of streaming data objects is based on the age, referred to herein as the forward age, of a data object measured from a landmark time in the past to a time associated with the occurrence of the data object, e.g., an object's timestamp. A forward time decay function is parameterized on the forward age. Because a data object's forward age does not depend on the current time, a value of the forward time decay function is determined just once for each data object. A scaling factor or weight associated with a data object may be weighted according to its decay function value. Forward time decay functions are beneficial in determining decayed aggregates, including decayed counts, sums, and averages, decayed minimums and maximums, and for drawing decay-influenced samples.

Abstract: A method and apparatus for providing an anonymization of data are disclosed. For example, the method receives a request for anonymizing, wherein the request comprises a bipartite graph for a plurality of associations or a table that encodes the plurality of associations for the bipartite graph. The method places each node in the bipartite graph in a safe group and provides an anonymized graph that encodes the plurality of associations of the bipartite graph, if a safe group for all nodes of the bipartite graph is found.

Abstract: A method and apparatus for providing an anonymization of data are disclosed. For example, the method receives a communications graph that encodes a plurality of types of interactions between two or more entities. The method partitions the two or more entities into a plurality of classes, and applies a type of anonymization to the communications graph.

Abstract: The present disclosure is directed to systems, methods, and computer-readable storage media for sampling from distributed data streams. Data elements are received at site servers configured to collect and report data to a coordinator device. The site servers assign a binary string to each of the data elements. Each bit of the binary strings can be independently set to a 0 or a 1 with a probability of one half. The binary string is used to sample from the received data elements, and the data elements and/or the sampled data elements can be transmitted to a coordinator device. The coordinator device can examine one or more bits of the binary string to draw samples of the received data elements in accordance with desired probabilities.

Abstract: The present disclosure is directed to systems, methods, and computer-readable storage media for anonymizing data over multiple temporal releases. Data is received, and nodes and connections in the data are identified. The data also is analyzed to identify predicted connections. The nodes, the connections, and the predicted connections are analyzed to determine how to group the nodes in the data. The data is published, and the grouping of the nodes is extended to subsequent temporal releases of the data, the nodes of which are grouped in accordance with the grouping used with the data.

Abstract: A method for validating outsourced processing of a data stream arriving at a streaming data warehouse of a data service provider includes a proof protocol. A verifier acting on behalf of a data owner of the data stream may interact with a prover acting on behalf of the data service provider. The verifier may calculate a first root hash value of a binary tree during single-pass processing of the original data stream with limited computational effort. A second root hash value may be calculated using the proof protocol between the verifier and the prover. The prover is requested to provide certain queried values before receiving random numbers used to generate subsequent responses dependent on the provided values. The proof protocol may be used to validate the data processing performed by the data service provider.

Abstract: Aggregates are calculated from a data stream in which data is sent in a sequence of tuples, in which each tuple comprises an item identifier and a timestamp indicating when the tuple was transmitted. The tuples may arrive at a data receiver out-of-order, that is, the sequence in which the tuples arrive are not necessarily in the same sequence as their corresponding timestamps. In calculating aggregates, more recent data may be given more weight by a decay function which is a function of the timestamp associated with the tuple and the current time. The statistical characteristics of the tuples are summarized by a set of linear data summaries. The set of linear data summaries are generated such that only a single linear data summary falls between a set of boundaries calculated from the decay function and a set of timestamps. Aggregates are calculated from the set of linear data summaries.