Patent ID: 12259890

DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS

In the following description, like parts are denoted by like reference numerals.

Processing a large dataset to produce indices and summary data can be time consuming. When an extended time series of data had finished collecting/recording, a user must wait for a large volume of data, typically terabytes, petabytes or even more, to be processed and indexed before they can begin analysing the data. Such processing may extend for hours or even days, depending on the volume of data. In many applications, in particular diagnostic or detection applications, it would be advantageous to be able to analyse recorded data as soon as possible, or to be able to conduct real time analysis during the data collection.

WO 2021/171018 A1 describes methods enabling indexing, aggregation and even browsing of time series of data, including radio IQ data, in real-time without interrupting reception and storage of the time series of data. The aggregation of the received data described in WO 2021/171018 A1 allows for browsing of the dataset almost immediately after finishing reception, or even during reception of the time series of data. In particular, the data may be visualised at different timescales using the pre-aggregated data. For raw data, for example radio IQ data, there are reasonably predictable ways to aggregate data, for example, total power corresponding to a given combination of a frequency range6F and time interval St.

However, to move beyond visualisation and browsing of raw data and enable comparable functionality for derived measurements, the number of possible permutations of search parameters expands very rapidly, so that it becomes impractical to pre-generate aggregations in the manner outlined in WO 2021/171018 A1. Firstly, because generating a large number of permutations of pre-aggregated views will make it challenging or impossible to keep up with real-time acquisition and storage of a time series. Secondly, because generating a large number of permutations of pre-aggregated views will require large amounts of additional storage for views which a user may, or may not, ever wish to view.

This specification concerns methods and hardware for analysis of time series of radio data. Any hardware and/or methods suitable to provide real-time acquisition and storage of a time series of radio data may be used in combination with the methods of analysis described herein. Real-time indexing of the time series of radio data is preferable, but not essential. For example, indexing, aggregation and even browsing of the time series of radio data may be implemented as described in WO 2021/171018 A1, the entire contents of which are hereby incorporated by reference. In particular, the system shown in FIG. 5 of WO 2021/171018 A1 may be used to implement the methods described in relation to FIGS. 2 to 4 of WO 2021/171018 A1.

The present specification concerns analysing the time series of radio data as it is obtained to identify any likely signals. Once a signal is identified, one or more predetermined or user specified signal properties of that signal are determined, and the signal and corresponding signal properties are stored to a dual-database structure described hereinafter. As will be explained, the dual-database structure includes a detailed database storing full information of each signal, and a summary database which may be used to rapidly and dynamically generate aggregations for visualising the signal properties in response to a user query. The methods of the present specification enable a user to begin analysing the derived data for automatically identified signals almost immediately after acquisition, or even during acquisition without interrupting reception and storage of the time series of radio data.

Although there are conventional methods for inserting new nodes into database structures, conventional methods typically require updating previously generated nodes. This can be time consuming, especially as the tree grows larger, and may be further complicated if each node should also point to corresponding summary data (which may require re-calculation if the node is updated). As already mentioned, pre-generated of summary/aggregated data is impractical for all possible permutations of more than a couple of a signal properties.

In contrast, the methods of the present specification are optimised for live, or real time, indexing, and also includes generating a summary database which enables user queries covering large numbers of signals to be processed without the need to revert to every individual signal record. Further distinctive details of the new method of indexing and analysing a time series of radio data in real-time shall become apparent from the descriptions hereinafter.

Where this specification refers to a binary tree structure, the following terminology may be used herein. A binary tree herein is formed from a number of binary tree nodes, each of which may have between zero and two edges which connect to binary tree nodes at a lower level of the tree structure. The number of edges leaving a binary tree node may be referred to as the degree of that binary tree node. A binary tree node having a degree of zero may be referred to as a leaf (plural leaves, sometimes also referred to as “end” nodes). The highest level binary tree node in a binary tree may be referred to as a binary tree root node. The intermediate binary tree nodes connecting between a binary tree root node and the binary tree leaves descending from that binary tree root node may simply be referred to as binary tree nodes (or alternatively as binary tree “branch” nodes). Binary tree nodes and binary tree root nodes may sometimes also be referred to as “internal” nodes, in contrast to binary tree leaves. Trinary tree structures are similar and use similar terminology, and differ from binary trees in that each node may have between zero and three edges. Similarly for Quaternary, and so forth to the general case of an N-ary tree structure (having between zero and a number, N, of edges).

The lower level nodes connected by edges to a node may be referred to as the children of that node, which itself may be referred to as the parent of the connected lower level nodes.

The depth of a node or leaf may refer to the number of edges which must be traversed moving from the root node to that node or leaf. The height of a node may refer to the number of edges which must be traversed moving from the deepest leaf to that node. The height may refer to the height of the root node of a tree structure. A level of a tree structure may refer to all the nodes or leaves having the same depth or same height. All nodes and leaves which may be reached from a particular node may be referred to as descendants of that node, and that node itself may be referred to as an ancestor of the descendant nodes and/or leaves.

Structures other than tree-structures are also referred to herein, and may be described having levels/heights and nodes/leaves by analogy to tree-structures. In particular, the detailed database described hereinafter with reference toFIGS.6A to7.

Referring toFIG.1, a system1is shown.

The system1includes one or more data sources in the form of radio receivers2in communication with an apparatus3via corresponding links4. The apparatus3includes one or more processors5, one or more storage devices6and optionally a communications interface7. Each processor5has associated volatile memory8accessible by that processor5. Each processor5preferably has a corresponding data storage device6, although this is not necessary depending on the bit rate of processed data output by the processor5. In other examples, more than one storage device6may correspond to a single processor (for example a Redundant Array of Independent Disks, or RAID, configuration). The number, M, of processors5is preferably equal to the number, Nr, of radio receivers2, so that each processor5may be dedicated to processing a time series of data from a particular radio receiver2. However, depending on the computational capacity of each processor5and the bit-rate from each radio receiver2, a processor5may process data from two or more radio receivers2in some examples for which Nr>M.

When the apparatus3includes two or more processors5, they may be integrated in a single data processing device or chip, for example sharing a common bus9(as illustrated inFIG.1) and/or volatile memory8. Alternatively, when the apparatus3includes two or more processors5, each processor5may be provided as a separate data processing device, optionally including one or more dedicated data storage devices6and volatile memory8, and the multiple data processing devices may be stacked (or racked) or otherwise interconnected to provide the apparatus3.

The system1includes a client which may take the form of a local client10integrated with or connected to the apparatus3, and/or one or more remote clients11connected to the apparatus3via the communications interface7and one or more wired or wireless networks12. The one or more wired or wireless networks12may include the internet.

Each local or remote client10,11is connected to a display13, or some other suitable output device, in order to present information received from the apparatus3to a user. Each local or remote client10,11may be configured to receive and/or generate user queries and to transmit the user queries to the apparatus3. A user query may be manually input by a user, for example by providing input to a graphical user interface (GUI) or using an electronic form. Alternatively, a user query may be automatically generated and submitted by an analysis program (not shown), or a graphical user interface used for data browsing being executed by the local or remote client10,11. For example, an analysis program (not shown) may utilise an application program interface (API) for data queries to formulate and submit user queries to the apparatus3. An analysis program (not shown) may include one or more machine learning models, for example, trained on human indexed training sets to identify particular signals or types of signals for follow-up, for example for review by a human user. In some applications, for example detecting unmanned aerial vehicles, UAVs, or “drones” approaching or encroaching on an airport perimeter, the analysis program may trigger automated follow-up using one or more cameras directed based on the radio data.

Herein, the term “user query” may be used to encompass manually entered user queries and also user queries which are automatically generated, for example by an analysis program (not shown) being executed by the local or remote client10,11.

Each processor5may be a single core processor or a multi-core processor. When two or more processors5are used, each processor5preferably does not correspond to different cores of a single processor unit/chip. Each processor5may take the form of one or more digital, electronic programmable central processing units (CPUs) capable of executing code read from a non-transitory medium or a memory (for example data storage device6and/or volatile memory8) to perform the functions and operations taught herein. Alternatively, each processor5and the corresponding volatile memory8may be provided by a corresponding microcontroller. In other examples, the methods described hereinafter may be performed using processors5and volatile memories8implemented using any suitable digital signal processing circuit, such as a system-on-a-chip, application specific integrated circuit (ASIC) or field-programmable gate array (FPGA).

Each data storage device6may take the form of one of more non-transitory computer readable media such as, for example, hard disc drives (HDD) and/or solid state drives (SSD). Each data storage device6may take the form of a number of HDDs configured as a Redundant Array of Independent Discs (RAID), or a number of SSDs arranged in a RAID configuration.

The links4connecting the radio receivers2to the apparatus3may take the form of physical connections such as, for example, Ethernet connections, optical fibre links, co-axial cables and so forth. Alternatively, some or all of the links4may take the form of wireless links. The links4may include one or more networks such as, for example, mobile phone networks, the internet, local area networks, and so forth. The links4should be sufficient to transfer the data bit-rate output by the radio receivers2. In applications where provision of reliable and/or sufficiently high bandwidth links4is not possible, processing may be devolved to data nodes (not shown) integrating a radio receiver2with the processing5, memory8and storage6necessary to perform some or all of the steps of the data processing methods described herein. Data may be periodically downloaded from data nodes (not shown) by visiting each data node (not shown) and establishing a direct connection or short range wireless connection to a communication interface of that data node (not shown).

A time series of data received from each radio receiver2of the respective link4may take the form of In-phase and Quadrature (IQ) data, spectrograms generated by applying overlapped Fast Fourier Transforms (FFTs) to the IQ data, power spectrum data, or any other data suitable for representing radio signals or spectra. The apparatus3may control the tuning frequency of each radio receiver2For example, user input via a GUI executed by a local or remote client10, ii may specify a range of frequencies for the radio receivers2. In some implementations a lower, start frequency f1and an upper, end frequency f2may be pre-set (for example loaded from a configuration file), or dynamically user defined using the GUI executed by a local or remote client10, ii.

The apparatus3may control the one or more radio receivers2to perform a frequency swept measurement spanning the range between the start and end frequencies f1, f2. A frequency sweep may be used when a radio receiver2has an instantaneous bandwidth ΔfIBW, which is less than the range f2−f1between start and end frequencies f1, f2. However, when the start and end frequencies f1,f2have a range smaller than the instantaneous bandwidth ΔfIBW, a frequency sweep may not be necessary.

When two or more radio receivers2are connected to the apparatus3, a frequency sweep may be performed faster by staggering the central frequencies fcto which each radio receiver2is tuned. Alternatively, when two or more radio receivers2are connected to the apparatus3, they may be tuned to have adjacent or overlapping instantaneous bandwidths ΔfIBW, increasing an effective instantaneous bandwidth for which a time series of radio data may be recorded. When two or more radio receivers2are tuned to have adjacent or overlapping instantaneous bandwidths ΔfIBWin order to provide an increased effective instantaneous bandwidth, the corresponding time series of radio data may be combined (e.g. concatenated in frequency) and processed as a single time series of data (for example, at each time the data may include pairs of frequencies and corresponding signal power and/or phase).

In a further alternative, each of two or more radio receivers2may be individually tunable to a different central wavelength fcin order to make independent measurements. When two or more radio receivers2are separately tuned for independent measurements, the data from each radio receiver2may be processed as a separate time series of data.

Method of Analysing and Indexing a Time Series of Radio Data

Referring also toFIG.2, a process flow diagram is shown for a method of analysing and indexing a time series of radio data.

The method ofFIG.2, and all subsequently described methods herein, are computer implemented. For example, using the apparatus3shown inFIG.1. Alternatively, methods described herein may be executed by any suitable data processing apparatus including one or more digital electronic processors communicatively coupled to random-access memory and computer readable storage, said data processing apparatus including or being communicatively coupled to one or more radio receivers in order to receive a time series of radio data.

The method illustrated inFIG.2does not relate to the reception, storage and optionally indexing of the radio data as received, for example the IQ data, power data and so forth. This may be carried out using any suitable method, including but not limited to the methods described in WO 2021/171018 A1, the entire contents of which are hereby incorporated by reference. For example, the system shown in FIG. 5 of WO 2021/171018 A1 may be used to implement the methods described in relation to FIGS. 2 to 4 of WO 2021/171018 A1.

Instead, the method illustrated inFIG.2concerns analysing the time series of radio data to detect signals, determining one or more signal properties of detected signals, and storing and indexing the signals and signal properties ready for swift browsing and/or querying.

A buffer of the time series of radio data is received (alternatively “filled”) (step S1). Let E(t) denote the radio data of the time series sampled at time t. The sample E(t) may take the form of IQ data (2D vector) from one or more radio receivers2. Alternatively, the sample E(t) may take the form of a list comprising frequencies f spanning between the start f1and end f2frequencies, each frequency having corresponding values of power and phase.

The method explained hereinafter may be readily adapted to any specific format or content of the samples E(t). Samples E(t) are typically obtained at regular sampling intervals δt, relative to a start time t0(for example the beginning of recording the time series of radio data), let E(t0+r·δt)=Erfor positive, integer r. The received buffer may have a size Rb so that the buffer received (step S1) may be denoted as BuffR={Er−Rb+1, Er−Rb+2, . . . , Er−1, Er}. A second buffer of the same length Rb may be filled with the time series of radio data whilst the received buffer period BuffR is processed. Depending on the processing speed available, the data rate and similar considerations, two, three or even more buffers may be maintained.

Following the end of each consecutive buffer period Rb·δt, the radio data BuffR={Er−Rb+1, Er−Rb+2, . . . , Er−1, Er} stored for the most recently elapsed buffer period Rb·δt is analysed to detect any signal which may be present (step S2). Any suitable method for automated signal detection in radio data may be used, for example including, but not limited to:Thresholding based on signal power (in time and/or frequency space);Correlation analysis to compare the radio data against one or more known signal types;Clustering analysis;Expert systems; and/orApplication of a machine learning algorithm trained using signals indexed by a human or any of the previously mentioned methods.

Referring also toFIGS.3A and3B, a comparison of received radio data (FIG.3A) and corresponding identified signals (FIG.3B) is shown.

FIG.3Apresents a 2-dimensional histogram (or “heat-map”) having frequency along the horizontal x-axis and time along the vertical y-axis, with the shading (z-axis) corresponding to total power in a given frequency and time bin. Lighter shades represent less power and darker shades indicate greater power.

FIG.3Bpresents a 2-dimensional histogram (heat-map) corresponding toFIG.3A, except that the shading corresponds to the number of signal detections in a given frequency and time bin. Lighter shades represent fewer detections and darker shades indicate more detections.

In general, there may be more than one signal concurrently active—in particular when the time series of radio data Erspans a range of frequencies f2−f1(as is typical). Let the total number of signals active detected during a buffer BuffR be denoted by K (a positive integer), then starting from a first signal (ordered from earliest to latest signal end time) (step S3), the kthof K signals is checked to determine whether it ends during the most recent buffer BuffR (step S4).

If the kthof K signals has ended (step S4Yes), then the signal data for that signal is stored to a detailed database DD (FIG.6B) and summary database SD (FIG.9) (step S5).

This includes determining a start time tstartand an end time tendof that signal, as well as determining one or more signal properties including at least a duration d=tend−tstart. Other signal properties may include (without being limited to), one or more of:A minimum frequency fmin(sometimes also referred to as a “start” frequency);A maximum frequency fmax(sometimes also referred to as an “end” frequency);A signal bandwidth δf=fmax−fmin;A centre frequency fc; and/orAn integrated signal power W.

The start time tstart, end time tendand the one or more signal properties of the signal are saved to the detailed database DD (FIG.6B) and summary database SD (FIG.9).

The detailed database DD (FIG.6B) includes a number 0≤n≤N−1 levels, with N an integer≥1 and n an index of level within the detailed database DD. Each level n includes a number Mn≥1 of detailed nodes DD(n,mn) (FIG.6C) with mnan integer index 1≤m≤Mn. The detailed node DD(n,mn) within each level n sub-divides time t into consecutive detailed node periods ΔtD(n,mn). Each detailed node DD(n,mn) has a corresponding node start time tDstart(n,mn) and end time tDend(n,mn) such that ΔtD(n,mn)=tDend(n,mn)−tDstart(n,mn).

Storing a signal to the detailed database DD includes storing the start time tstart, end time tendand the one or more signal properties into the lowest level n available detailed node DD(n,mn) corresponding to a detailed node time period ΔtD(n,mn) encompassing the start time tstartand end time tend of that signal, i.e. tDstart(n,mn)≤tstart≤tDend(n,mn) and tDstart(n,mn)≤tend≤tDend(n,mn). One suitable example of a method for storing signal data to the detailed database DD (and in the process building the detailed database DD) is shown inFIG.7and explained hereinafter.

The summary database SD includes two parts, an auxiliary database AD (FIG.13C) and a tree-structure of summary nodes SD(n,mn) (FIG.9) having a plurality of levels n, 0≤n≤N−1. Note that the number N of levels n need not be identical between the detailed DD and summary SD databases, however the same index variables are used herein because which database is referenced shall be clear from the context. The summary nodes SD(n,mn) at each level correspond to consecutive summary node periods ΔtS(n,mn) of equal length. Each summary node SD(n,mn) has a corresponding node start time tSstart(n,mn) and end time tSend(n,mn) such that ΔtS(n,mn)=tSend(n,mn)−tSstart(n,mn). Each summary node stores a summary data structure, denoted SUM herein. The summary data structure SUM stores, for each unique combination of quantised (or “binned”) signal property values, a count of the number of signals corresponding to the respective summary node period which have that unique combination of quantised signal property values.

For example, let p1, p2, . . . , pPdenote a number P of signal property values measured for each signal. One approach to the summary data structure SUM would be to use a P-dimensional array (or alternatively a P-dimensional histogram), having corresponding counts of signal numbers as entries. This would typically be very sparse and memory intensive, so preferably each time a new unique set of properties {p1, p2, . . . , pP} is detected (up to the width of quantisation/binning), a new row may be added to a list specifying the set of properties {p1, p2, . . . , pP} and an associated count value C (as shown inFIG.10). More preferably, the summary data structure SUM is stored as a hashmap. The signal start tstartand end tendtimes are not stored in the summary data structure SUM, since this would in practice prevent the intended aggregation of signal property data.

Storing a signal to the summary database SD includes, at each level of the summary database, either:updating, based on the signal properties {p1, p2, . . . , pP}, the summary data structures SUM of one or more summary nodes SD(n,mn) having summary node periods ΔtS(n,mn) overlapping the duration d=tend−tstartof that signal; orstoring the signal data (i.e tstart, tend, {p1, p2, . . . , pP}) corresponding to that signal in the auxiliary database AD.

Examples of methods for storing signal data to the summary database SD (and in the process building the summary database SD) are shown inFIGS.11and12, inFIGS.15and16, and inFIGS.20and21, and each is explained in further detail hereinafter.

Returning to refer in particular toFIG.2, if the kthof K signals has not ended (step S4|No), then that signal is tracked to the next buffer period (step S6). The tracking of signals may be based on, for example, maintaining a frequency sorted list of active signals. This may include determining a start time tstart(or propagating one determined from a previous buffer period), and also determining one or more properties of the partially captured signals, for example a central frequency fcor a bandwidth δf, in order to allow matching a tracked signal between successive buffer periods. Additionally or alternatively, each buffer BuffR may be configured to have a number of samples at the start which correspond to (or overlap) the final samples of the preceding buffer BuffR, to allow matching up signals between periods using correlation analysis.

If there are further detected signals to consider (step S7|Yes), the next signal detection is processed (steps S8and S4). Otherwise, provided the collection of the time series of radio data continues (step S9|Yes), the next buffer BuffR is received (step S1) and the processing repeated.

Referring also toFIG.4, an example of tracking and processing signal detections across a number of buffer time periods Rb·δt is shown.

In the first buffer period t0≤t<t1, three signals S1, S2and S4are detected (step S2), so that all of them start in the period, K=3. A first signal S1also ends (step S4|Yes) during the corresponding buffer BuffR and is stored to the databases DD, SD (step S5). However, the second S2and third S3signals (presumed to be distinguishable, for example by frequency) do not end (step S4|No) and are instead tracked (step S6) to the next buffer period t1≤t<t2. It may be concluded that a detected signal has ended by, for example, looking for a period of baseline/background power in the corresponding frequency range.

In the second buffer period t1≤t<t2, there are again a total of K=3 signal detections. The second signal S2ends and is stored, as does the new, fourth signal S4, however the third signal S3continues and is again tracked to the next buffer period.

In the third buffer period t2≤t<t3, there are a total of K=2 signal detections. A new fifth signal S5starts but also ends, and is processed. The third signal S3is once again tracked to the following, fourth buffer period t3≤t<t4. No signals end in the fourth buffer period t3≤t<t4, and the third signal S3continues to be tracked.

In the fifth buffer period t4≤t<t5, the third signal S3ends, and is processed, whilst a sixth signal S6is detected but does not finish. The sixth signal S6is tracked into the sixth buffer period t5≤t<t6, during which it finishes and is processed.

In the examples described herein, the summary database SD includes a binary tree index. However, this is not essential, and in variations of the methods described herein the summary database may use a trinary tree index, a quaternary tree index, and so forth.

Expressed alternatively, in the general case the summary database SD includes an N-ary tree index with index n≥2.

The auxiliary database AD may have the same structure as the detailed database DD. Further examples are explained in relation to methods illustrated inFIGS.11and12, inFIGS.15and16, and inFIGS.20and21.

The leaf node period ΔtS(o,mn) corresponding to each summary node SD(o,mn) at the leaf (lowest) level (n=0) may also be referred to herein as a “base time period”.

The quantisation of signal property values p1, P2, . . . , pPfor the purposes of the summary data structure SUM may alternatively be described as binning. The quantisation (or binning) need not be the same at each level n of the summary database SD. In some implementations, the quantisation (or binning) may become coarser (larger bins) as the level n of the summary database SD increases. As an example, if frequency is binned into bandwidths δfbin(for example a minimum resolution determined by the sampling interval δt in the usual way) at the lowest level n=0, then at a higher level n>0, the frequency binning may switch to an integer multiple of δfbin.

Although there is no reason why the methods described herein could not be applied to index and analyse signal detections obtained from an already collected time series of radio data Er, the advantages of the presently described methods are most apparent when the time series of radio data Eris received and stored in real time. The methods described herein enable that detection of signals and storage of detected signals to the detailed database DD and summary database SD can occur at the end of each buffer period Rb·δt without interrupting the reception and storage of the time series of radio data Er. In particular, this is because the detailed database DD and summary database SD may be built up in real time. This is possible because detailed databases DD and summary databases SD described herein are built up based on the most recently received data Er, and require no, or only minimal, changes to previously generated nodes of the respective databases SD, DD. In this way, indexing of the received time series of radio data Ercan keep up with the reception of the time series of radio data Er, allowing near immediate browsing and analysis once collection is completed. For example, the dataset may be browsed and/or searched at different timebase resolutions, and for any combination of the measured signal properties p1, p2. . . , pP, without the need to return to search and analyse the individual signal detections each time the parameters of a visualisation are changed.

Additionally, in some examples the already generated sections of the detailed database DD and the summary database SD may be used to implement browsing, querying and analysis of data during the reception of the time series of radio data Er, without interruption. In this way an operator monitoring a radio spectrum in a region/area may obtain real-time insights into what is occurring, without interrupting recording of the data and potentially missing detections.

In some examples, each summary node SD(n,mn) and each detailed node DD(n,mn) is read only once the current time t is after the respective node end period tSend(n,mn), tDend(n,mn).

This need not be explicitly controlled, it is more that the presently described methods focus on the leading edge of time, and do not require doubling back to modify earlier (or “closed”) nodes SD(n,mn), DD(n,mn) of the databases.

Method of Querying the Detailed Database and Summary Database

Referring also toFIG.5, a process flow diagram is shown for a method of querying the detailed database and summary database generated using the methods of the present specification (for example, as shown inFIG.2).

A query is received or generated (step S10). As explained hereinbefore a query may be user generated or automatically generated. In an example of a user generated query, a user may wish to generate a 2D histogram plotting, for example, peak signal power against signal duration, for signals occurring within a given time period and frequency band. In another example, a user may use controls to scroll through and/or change a timebase displayed in a GUI for visualising the dataset, and the query may be formulated automatically to update the visualising based on the users input. In other examples, a query may be automatically generated by an analysis program as described hereinbefore.

A query used for explanations herein is made up of query fields including (but not limited to):a query start time tQstart;a query end time tQend; andone or more ranges Δp1, Δp2, . . . , ΔpPof the signal properties p1, p2, . . . , pP.

The ranges Δp1, Δp2, . . . , ΔpPof the signal properties p1, p2, . . . , pPdo not all need to be used for searching, i.e. only a subset of signal properties p1, p2, . . . , pPmay be searched (equivalently, some of ranges Δp1, Δp2, . . . , ΔpPmay be set to span the entire dataset). The difference between the query start time tQstartand the query end time tQendis a query time period ΔtQ=tQend−tQstart.

In response to meeting a condition based on the query fields (step S11|Yes), the query is processed using the summary database SD (step S12), and otherwise (step S11|No), the query is processed using the detailed database DD (step S13). An example of processing a query using the detailed database DD is described hereinafter in relation toFIG.8. Examples of processing a query using the summary database SD are described hereinafter in relation toFIGS.14and18.

The purpose of the condition is to gauge the likely number of signals which may match the query, at least to about the right order of magnitude. Queries likely to return a number of results which is manageable to process in reasonable time using the individual signal detections (i.e. without undue lag from a user's perspective) are processed using the detailed database DD. In practice, number of results which may be manageably processed depends on the computing resources available, and may vary between about 105and 107. If the likely number of results will be sufficiently large to potentially cause lag/slowdown, then the summary database SD is used to process the query. This is enabled because queries likely to return hundreds or thousands, if not millions or more signal detections, will in any event require aggregation before they can be meaningfully visualised. Therefore, such queries may be resolved using the summary data structures SUM of one or more summary nodes SD(n,mn) without loss of accuracy.

One example of a suitable condition is the query time period AtQ. The longer the query time period AtQ, the larger the number of individual signal detections within the bounds of the query is likely to be. A query period threshold Δtthreshmay be chosen, and the condition may be applied so that for AtQ≤Δtthreshthe detailed database DD is used (step S13), whilst for AtQ>Δtthreshthe summary database SD is used instead. The query period threshold Δtthreshmay be predetermined or may be dynamically determined, for example, based on the processing power of hardware executing the method. In other examples, the query period threshold Δtthreshmay be user specified.

Different conditions may be applied based on the query fields specified. For example, if a query is specified spanning the entire time period of the dataset but looking for signals in a queried frequency band fQ1to fQ2, the total number of signals in that frequency band fQ1to fQ2may be estimated by adding up the counts of the summary data structure SUM for the root summary node SD(N−1,1) which overlap the queried frequency band fQ1to fQ2. If that number exceeds a threshold, the summary database SD is used (step S12), whereas if that number is less than or equal to the threshold, the detailed database DD may be used.

In general the goal is to use the detailed database DD up until doing so would be too inefficient in time. This may correspond to a noticeable lag for a user generating the queries whilst browsing the signal detections from the time series of radio data Er. For example, a user may begin to perceive a lag if the time for processing a new query and plotting a visualisation (for example due to scrolling or zooming in a GUI viewing the signal data) started to exceed about 80 ms. The factors which determine how much time the querying of the detailed database will take are dominated by the number of results that will be found by the query, in conjunction with how fast the hardware system is in general (combination of storage read speed and computing processing power). The conditions described hereinbefore, and similar conditions, represent estimating the total number of results which will require processing based on sensible guesses (time threshold Δtthresh) and root/high level processing of the summary database SD. This represents a good balance between efficiency and detail.

In other examples, the condition (step S11) may be based on a lighter weight tertiary data structure, calculated based on the summary data structures, and configured to allow evaluating an indication or upper bound on the number of results a query may produce, or any other function for estimating the number of detections the query will produce.

The querying method shown inFIG.5may be executed after the reception and recording of the time series of radio data Erhas finished. However, the query method and the structures of the detailed database DB and summary database permit querying of the partially completed databases DD, SD during reception and processing of the time series of radio data Er.

Method of Storage to the Detailed Database

Referring also toFIGS.6A to7, an exemplary method of building the detailed database DD is shown, and illustrated in relation to a worked example.

FIG.6Aschematically illustrates a set of signals in terms of start tstartand end tendtimes.FIG.6Bschematically illustrates the contents of a detailed database formed by applying the method ofFIG.7to the example shown inFIG.6A.FIG.6Cschematically illustrates the structure of a single detailed node DD(n,mn).FIG.7shows a process flow diagram of a method of forming the detailed database.

Referring in particular toFIG.6C, in this example each detailed node DD(n, mn) stores up to a threshold number Kmaxof signals. Each signal is stored as signal data14which includes at least the signal start time tstart, the signal end time tend, and the values of the one or more signal properties p1, p2, . . . , pP. The threshold number Kmaxof signals which each detailed node DD(n, mn) can store may be a function of level n, i.e. Kmax(n), and may be predetermined or set by a user prior to starting to receive and process the time series of radio data Er.

Each level of the detailed database DD may include one active detailed node DD(n,mn) having only a node start time tstart, and any number of closed detailed nodes DD(n,mn), each having a node start time tstartand a node end time tenddefining the corresponding detailed node period ΔtD(n,mn). For example, referring in particular toFIGS.6A and6B, at the end of the period shown Gust after t11), the detailed database DD includes:At level n=0, DD(0, 4) is the active detailed node whilst DD(0, 1), DD(0, 2) and DD(0, 3) are closed detailed nodes;At level n=1, DD(1, 2) is the active detailed node whilst DD(1, 1) is a closed detailed node; andAt level n=2, DD(2,1) is the active detailed node, and there are not yet any closed detailed nodes.

The detailed database DD shown inFIG.6Bis not a tree structure—the detailed nodes DD(n,mn) at level n=1 do not point to the detailed nodes DD(n,mn) at level n=0, and are not pointed to by the detailed nodes DD(n,mn) at level n=2. Consequently there is no requirement for the detailed node start tDstart(n,mn) and end tDend(n,mn) times, or the corresponding detailed node periods ΔtD(n,mn), of one level n to have any relationship to those of the adjacent levels n±1.

Referring in particular toFIG.7, the method of building the detailed database follows detection of a signal (step S2) and determination of the signal data14(step S4) including the at least the signal start time tstart, the signal end time tend, and the values of the one or more signal properties p1, p2, . . . , pP.

The signal data14is received (step S16). For example, at time t, inFIG.6A, signal data14corresponding to signal S1is received, or at time t8, signal data14corresponding to signal S8is received, and so forth.

Starting from the lowest level (step S17) of the detailed database DD, it is tested whether the start time tstartof the signal is before the node start time tDstart(n,mn) of the current active detailed node DD(n,mn) at the searched level n (step S18). The detailed database is originally initialised with a single node D(0,1), which is active with a node start time tDstart(0,1) equal to the start time toof the time series.

If no (step S18|Yes), i.e. if the signal start time tstartis equal to or after the node start time tDstart(n,mn), the signal data14of the signal are stored to the active node DD(n,mn) (step S19). Optionally, the section of the time series data Ercorresponding to the signal may also be included in the signal data14(for example IQ data). Alternatively, the signal data14may optionally include a pointer to the start of the signal in the time series data Er.

After saving signal data14to a detailed node DD(n,mn), it is tested whether that node DD(n,mn) is now full, i.e. if the numbers of signal data14stored equals the threshold Kmax(step S20). If the node is not full (step S20|No), then we proceed (step S7) to the next signal or buffer BuffR as appropriate. However, if the node is full (step S20|Yes), then the current node DD(n,mn) is closed by assigning a detailed node end time tDend(n,mn) equal to the signal end time tend(step S21), incrementing the index mnat the searched level to mn+1 (step S22), and opening a new active node DD(n,mn) by assigning the detailed node start time tDstart(n,mn) equal to the signal end time tend.

Returning to step S18, if the start time tstartof the signal is before the node start time tDstart(n,mn) of the current active detailed node DD(n,mn) at the searched level n (step S18|Yes), then the searched level is incremented to n+1 (step S24). If the first node DD(n,1) at the new searched level exists (step S24|Yes), the test (step S18) is repeated. If the first node DD(n,1) at the new searched level does not yet exist (step S24|No), the index mnis initialised mn=1 (step S26) and the first node DD(n,1) at the new searched level n is opened as an active node, with the node start time tDstart(n,mn) set equal to the start time to of the time series of radio data Er(step S27).

The method ofFIG.7may be better understood by reference to the example shown inFIGS.6A and6B, in which the threshold storage capacity for all levels of detailed nodes DD(n,mn) is set to Kmax=2 signal data per node.

At the end of signal S1at time t1, the corresponding signal data14is stored to active detailed node DD(0,1) having start time tDstart(n,mn)=t0(step S18evaluates to ‘no’ for n=0, followed by steps S19and S20|No). At the end of signal S2at time t2, the corresponding signal data14is again stored to active detailed node DD(0,1). However this time the node if full (step S20|yes), so the detailed node DD(0,1) is closed with node end time tDend(0,1)=t2(step S21), and the next detailed node DD(0,2) is opened with node start time tDstart(0,2)=t2.

In similar fashion, signal data14corresponding to signals S3and S4are stored to detailed node DD(0,2), which is then closed and a new detailed node DD(0,3) is opened with node start time tDstart(0,3)=t4. When signal S5ends at t5, the start time tstartis before t4, meaning that signal S5cannot be stored to level n=0 (step S18Yes), and the first detailed node DD(1,1) is opened at level n=1 (steps S24through S27) so that signal data14of signal S5may be stored to it.

Continuing thoughFIG.6A, signal S6can be stored to DD(0,3). Signal S7is stored to and completes DD(1,1), and a new active detailed node DD(1,2) is opened on level n=1. Signal S8is stored to and completes DD(0, 3), and a new detailed node DD(0, 4) is made active. Signal S9cannot be stored to either of the active nodes DD(0, 4) or DD(1, 2), so it is necessary to open a first, active node DD(2, 1) at level n=2 to store the corresponding signal data14. Signal S10cannot be stored to active node DD(0, 4), but can be stored on the level above n=1 in DD(1, 2). Finally for the signals shown inFIG.6A, signal Su is stored into the level n=0 active node DD(0,4), which still has capacity for another set of signal data14.

The method shown inFIG.7uses specific tests and orders of operations. However, any other suitable method may be used instead. A suitable method will include searching successively increasing levels of the detailed database DD until an active detailed node DD(n,mn) at the searched level n has a node start time tDstart(n,mn) before the start time tstartof the signal, and in response storing signal data14corresponding to that signal to the active detailed node DD(n,mn) at the searched level n. If the active detailed node DD(n,mn) at the searched level n is then storing a number of signals equal to the corresponding threshold number of signals Kmax, a suitable method will also include closing the active detailed node DD(n,mn) at the searched level n by assigning the end time tendof that signal as the node end time tDend(n,mn), and opening a new active detailed node DD(n,mn+1).

The method ofFIG.7does not generate a tree structure. Instead, each level n of the detailed database DD is effectively independent of the levels n±1 below and/or above. However, this is not essential and in other examples the detailed database DD may take the form of a tree-structure (not shown), for example binary, ternary and so forth. In such examples, the detailed node time periods ΔtD(n,mn) of a level n will be equal to each other, and also to a multiple of the node time periods ΔtD(n−1,mn−1) of the level n−1 below (excepting a leaf level). Such tree-structured detailed databases DD can still be built in real-time as data is obtained, for example, using the methods disclosed in WO 2021/171018 A1 (referring in particular to the methods described in relation toFIGS.2to4).

Method of Processing a Query Using the Detailed Database

In the general case of processing the query using the detailed database DD, starting from the lowest level of the detailed database DD, successively increasing levels n of the detailed database DD are searched and signal data14of each signal which matches all the query fields tstart, tend, and one or more ranges Δp1, Δp2, . . . , ΔpPof the signal properties p1, p2, . . . , pPis added to a detailed query result qDres. A range ΔpPdoes not need to be specified for every signal property pP, for example a user may wish to recover all signals having frequencies within a given range, without concern about any other signal properties pp.

Referring also toFIG.8, a process flow diagram is shown for a method of processing a query using a detailed database DD corresponding to the detailed database DD structured as illustrated inFIGS.6B and6Cand produced using the method ofFIG.7.

When processing the query using the detailed database DD (i.e. following step S11|No inFIG.5), starting from the lowest level n=0 of the detailed database DD (step S28), at each level n a search (for example a binary search) of that level n is conducted (step S29), and the signal data14of each signal which matches all the query fields tstart, tend, and the one of more ranges Δp1, Δp2, . . . , ΔpPof the signal properties p1, p2, . . . , pPis added to the detailed query result qDres(step S30). If the searched level n is not the maximum level N−1 (step S31|No), the level is incremented to n+1 and the next level searched. Once the searched level n is the maximum level N−1 (step S31|Yes), the detailed query result qDresis output (step S33) for display/storage etc (step S14).

In this way, each level n may be swiftly searched, and results from any/all of the levels 0≤n≤N−1 are found and added to the detailed query result qDres.

A signal (i.e. the corresponding signal data14) matches all of the query fields if it has a start time tstartafter the query start time tQstart, has an end time tendbefore the query end time tQend, and has signal properties p1, p2, . . . , pPwithin the corresponding received one or more ranges Δp1, Δp2, . . . , ΔpPof the signal properties p1, p2, . . . , pP. Δp1, Δp2, . . . , ΔpPof the signal properties p1, p2, . . . , pP

The detailed query result qDresmay include, or take the form of, a list of the start times tstart, end times tendand corresponding signal properties p1, p2, . . . , pPof each signal found in the detailed database DD matching the query fields tstart, tend, Δp1, Δp2, . . . , ΔpP.

The method of processing a query using the detailed database DD may include aggregating one or more signal properties p1, p2, . . . , pPstored in the detailed query result qDres. The signal properties p1, p2, . . . , pPto be aggregated may be predetermined, or may be selected by a user when preparing the query at the time of running the query. Signal properties p1, p2, . . . , pPmay be aggregated to generate, based on the values stored in the detailed query result qDres, one or more of histograms, bar charts scatter plots, surface plots and so forth. For example, two-dimensional histograms as shown inFIGS.3A and3B. Aggregating may include using the detailed query result qDresto populate a aggregated data structure Daggwhich stores, for each unique combination of a subset of the quantised signal property values p1, p2, . . . , pP, a count of the number of signals in the detailed query result which have that unique combination of the subset of the quantised signal property values p1, p2, . . . , pP. In other words, the aggregated data structure Daggfor the detailed query result qDresmay be analogous to the summary data structures SUM of the summary database, except populated using the detailed query result qDresinstead of the signals corresponding to a summary node period ΔtS(n,mn).

The aggregated data structure Daggmay be updated every time a new signal matching the query fields is found in the detailed database DD and added to the detailed query result qDres. Alternatively, the aggregation of one or more signal properties may be updated in response to the number of results added to the detailed query result qDressince last updating the aggregation is equal to a threshold value. A list is preferably reset after updating the aggregation of one or more signal properties p1, P2, . . . , pPin the aggregated data structure Dagg.

First Method of Storage to the Summary Database

Referring also toFIGS.9to12, a first exemplary method of building the summary database SD is illustrated.

FIG.9schematically illustrates the structure of the tree structure of the summary database, andFIG.10schematically illustrates the structure of a single summary node SD(n,mn).

FIG.11shows a process flow diagram of the first exemplary method of building the summary database SD, andFIG.12shows a process flow diagram for a method of propagating data to higher levels n within the tree structure (used at step S40inFIG.11).

Referring in particular toFIG.9a binary tree structure of summary nodes SD(n,mn) will be referred to in explaining the first exemplary method. However, other types of tree structure may be used instead, such as ternary, quaternary and so forth. Each leaf summary node SD(0,mn) has an equal duration ΔtS(0,mn)=Δτ. In the first exemplary method, the summary database SD comprises, at the lowest level, up to a number Mbof active leaf summary nodes SD(0,mn) corresponding to a summary buffer period Δtbuff=MbΔτ, and any number of closed leaf summary nodes corresponding to earlier summary node periods. The summary buffer period Δtbuffis independent of the buffers BuffR used in acquiring the time series of radio data Er.

For example, referring in particular toFIG.9, if the present time t is during leaf summary node SD(0,8), i.e. τ7≤t<τ8, then a summary buffer of length Mb=4 would include nodes SD(0, 5), SD(0, 6), SD(0, 7) and SD(0, 8). The summary buffer period Δtbuffis important for the handling of relatively longer signals in the first exemplary method.

Referring in particular toFIG.10, each summary node SD(n,mn) includes a summary data structure SUM. In the example illustrated, the summary data structure SUM takes the form of a listing, wherein the jthof J unique combinations p1,j, p2,j, . . . , pP,jof the signal properties p1, p2, . . . , pPstored in the structure SUM has a corresponding count value Cy and carry-over value COj. The carry-over values COjare only used internally in the first exemplary method. In practice, the same data need not be stored as a list, and may instead be stored as an array, a hashmap, or any other suitable data storage structure.

Referring in particular toFIG.11, the first exemplary method shall be explained. For the sake of explanation, SD(0,m) corresponds to the most recent leaf summary node, and SD(0,m−Mb+1) corresponds to the oldest. The leaf summary nodes SD(0,m−Mb+1), . . . , SD(0,m−1), SD(0,m) are the active summary nodes, and all other summary nodes at the leaf level n=0 are closed.

The first exemplary method of building the summary database follows detection of a signal (step S2) and determination of the signal data14(step S4) including the at least the signal start time tstart, the signal end time tend, and the values of the one or more signal properties p1, p2, . . . , pP.

The signal data14is received (step S34). If the start time tstartand end time tendof the signal are wholly within the summary buffer period ΔtB(step S35Yes), then the summary data structures SUM are updated for every active leaf summary node SD(0,mo−Mb+1), . . . , SD(0,mo−1), SD(0,mo) having a summary node period ΔtS(o,mo) overlapping the duration d=tstart−tendof the signal (step S36). For example, assuming that the level of quantisation in the summary data structures SUM corresponds to the smallest measurable difference in the signal properties p1, p2, . . . , pPof the signal, if a matching row p1,j, p2,j, . . . , pP,jalready exists, the count value Cjis simply increased by one, i.e. Cj→Cj+1. However, if a matching row p1,j, p2,j, . . . , pP,jdoes not already exist, it is added and the new count value Cjinitialised at one, i.e. Cj=1.

The carry-over values CO; for each row denoting how many signals having the identical combination of signal properties p1, p2, . . . , pP(up to the quantisation/binning) were also present in the preceding summary node SD(n,mn). For example, if a signal started during SD(o,m−M+1) and finished during SD(o,m−2), then for the corresponding combination of signal properties p1, p2, . . . , pPin the summary data structures SUM, one would be added to the count values C in each of the summary nodes SD(0,m−M+1) to SD(0,m−2), but the carry-over count would only be incremented for each of the summary nodes SD(0,m-M+2) to SD(0,m−2). In other words, the carry-over count is not incremented in the summary data structure SUM corresponding to the leaf summary node SD(o,mn) during which a signal started. These carry-over counts CO; are used in the first method of propagating summary data illustrated inFIG.12(and further described hereinafter).

If the start time tstartand end time tendof the signal are not wholly within the summary buffer period ΔtB(step S35|No), and the end time tendof the signal is within the summary buffer period ΔtB(i.e. the start time tstartof the signal is before the summary buffer period ΔtB) (step S37|No), then the signal data14of the signal is stored to an auxiliary database AD (step S38). The auxiliary database AD is preferably built using the same structure and methods as the detailed database, for example, using the structure and methods shown inFIGS.6B,6C and7. Whilst a significant majority of signals can be captured in the summary data structures SUM of the summary nodes SD(n,mn) when the durations are set appropriately, some cannot and are stored in the auxiliary database to prevent loss of data fidelity. This approach allows aggregating nearly all data, meaning that the number of signals leftover in the auxiliary database remains manageable.

If the start time tstartand end time tendof the signal are not wholly within the summary buffer period ΔtB(step S35|No), and the end time tendof the signal is after the summary buffer period ΔtB(step S37|Yes), the buffer period ΔtBis advanced and the checks (step S35, S37) are repeated until either:the summary data structures SUM are updated for every active leaf summary node SD(0,mo−Mb+1), . . . , SD(0,mo−1), SD(0,mo) having a summary node period ΔtS(0,mo) overlapping the duration d=tstart−tendof the signal (step S36); orthe signal data14corresponding to the signal is stored into the auxiliary database AD.

In the particular example shown inFIG.11, the shifting of the buffer period ΔtBis accomplished by adding the next leaf summary node SD(0,mo+1) (step S39) and, provided the summary buffer is already full (step S40Yes), The data in leaf summary node SD(0,mo−Mb+1) is propagated to levels n>0 (step S41). The propagation step (step S41) may also include adding a new top level (i.e. increasing N), and is described in further detail inFIG.12. The carry-over values COjare used when propagating data to prevent overcounting signals. Once data has been propagated to levels n>0 (step S41), the buffer is shifted by incrementing the index mo→mo+1 (step S42) before returning to the first test (step S35). If the buffer is not yet filled (step S40|No), the propagation (step S41) is skipped until the buffer is filled (this is only relevant in the first Mbleaf summary node periods ΔtS(0,mn)).

The oldest summary node SD(o, mo-Mb+1) may also be written/saved to a storage device6, or flagged as read only, when shifted out of the summary buffer. In practice this may not require doing anything specific beyond carrying out the steps (39to S42).

The propagation of data to levels n>0 (step S41) generally involves propagating the corresponding summary data structure SUM of node SD(0,mo−Mb+1) to all higher level n>0 summary nodes SD(n>0,mn) from which the oldest active leaf summary node SD(0,mo−Mb+1) is a descendant. The propagation (step S41) must enforce the condition that each summary node SD(n>0,mn) only counts each signal once in the respective summary data structure SUM. This is accomplished in the first exemplary method by using the carry-over counts COjto track signals extending over multiple summary nodes SD(n,mn).

For example, referring in particular toFIG.12, the propagation of summary node SD(n,mn) data to higher levels shall be described.

The method of propagation may be carried out relative to any level n, and indeed is carried out at step S49of the method illustrated inFIG.12. In other words, the process of propagation of data in summary nodes SD(n,mn) to higher levels n>0 may be recursive as needed.

Data is only propagated to the most recent summary node SD(n,mn) for n>0, with that node being added if necessary.

A command is received to propagate data of a node SD(n,mn) to higher levels (step S43). For example, this is applied to the summary node SD(0,mo−Mb+1) at step S41inFIG.11, and to the data of a summary node SD(n+1,mn+1) at step S49ofFIG.12. The data to be propagated is at least the summary data structure SUM, but may include further data in other examples.

It is tested whether the summary node time period ΔtS(n,mn) of the propagated node SD(n,mn) fits wholly within summary node time period ΔtS(n,mn) of the most recent summary node SD(n+1,mn+) at the next level up n+1 (step S44). In other words, whether tSstart(n+1,mn+1)<tSstart(n,mn)<tSend(n+1,mn+1) and tSstart(n+1,mn+1)<tSend(n,mn)≤tSend(n+1,mn+).

If the summary node time period ΔtS(n,mn) of the propagated node SD(n,mn) wholly fits (step S44|Yes) and the node at level n+1 is newly generated (step S45|Yes), i.e. created since step S43of the current execution of the propagation method, then the data from the propagated node SD(n,mn) is added to the node SD(n+1,mn+1) (step S46). In other words, the summary data structure SUM of propagated node SD(n,mn) is added to the (in this case blank) summary data structure SUM of node SD(n+1,mn+1).

However, if the summary node time period ΔtS(n,mn) of the propagated node SD(n,mn) wholly fits (step S44|Yes) but the node at level n+1 already existed (step S45|No), the data from the propagated node SD(n,mn) is added to the node SD(n+1,mn+1) with the carry-over values COjsubtracted (step S47). In other words, the summary data structure SUM of propagated node SD(n,mn) is added to the summary data structure SUM of node SD(n+1,mn+1) after first subtracting the carry-over values COjfrom corresponding count values Cy. Carry-over values COjare also used for levels n>0 to prevent overcounting when propagating from level n to n+1.

When the summary node time period ΔtS(n,mn) of the propagated node SD(n,mn) does not wholly fit within the summary node time period ΔtS(n,mn) of the most recent summary node SD(n+1,mn+1) at the next level up n+1(step S44|No), the most recent summary node SD(n+1, mn+1) at level n+1 is closed (step S48). This may involve writing the summary node SD(n+1, mn+1) to a storage device6or flagging as read only, but in practice may not require doing anything specific beyond carrying out the following steps (S49to S51). The data in summary node SD(n+1, mn+1) is propagated to levels n+2 and higher (step S49), which is in practice a command (step S43) to propagate node SD(n+1, mn+1). Once data has been propagated to levels n+2 and higher (step S49), the most recent summary node SD(n+1,mn+1) is shifted by adding the next summary node SD(n+1,mn+1+1) (step S50) and incrementing the index mn+1→mn+1+1 (step S51) before returning to the initial test (step S44).

Alternatively, any other suitable method for propagating the summary data structures SUM to higher levels n>0 whilst avoiding double counting of signals may be used.

Referring also toFIGS.13A to13C, a worked example of applying the first exemplary method of building the summary database SD will be explained.FIG.13AreproducesFIG.6Afor convenience in comparing withFIGS.13B and13C.FIG.13Bschematically illustrates the tree structure of the summary database SD generated for the signals shown inFIG.13Ausing the first exemplary method, andFIG.13Cschematically illustrates the auxiliary database of the summary database SD generated for the signals shown inFIG.13Ausing the first exemplary method.

InFIGS.13B and13C, the notation S4,5,6denotes for brevity the signals S4, S5and S6. Similarly, S6,8denoted signals S6and S8, and so forth. A buffer size Mb=4 was used for the following.

Initially, only one summary leaf node SD(0,1) exists. At time t1=τ1, the first signal S1ends. The index mo=1 and signal S1is wholly within the period ΔtS(0,1) (step S35|Yes). The signal S1only overlaps leaf summary node SD(0,1) (and there are not yet other leaf summary nodes SD(0,mo) in the summary buffer), so the signal properties p1, p2, . . . , pPof signal S1are added to the summary data structure SUM of leaf summary node SD(0,1) (step S36).

Signal S2ends at time t2, and as not yet updated the index mo=1 still. Therefore, signal S2is not wholly within the summary buffer period ΔtB=ΔtS(0,1) (step S35|No), and ends after the present end of ΔtBwhich is at τ1(step S37|Yes). Through steps S39to S42, new leaf summary node SD(0,2) is added, and as the summary buffer is not yet full (step S40|No), propagation (step S41) is skipped and mois updated to mo=2 (step S42). The end of the summary buffer is now at τ2, corresponding to both SD(0,1) and SD(0,2) being within the summary buffer. The signal S2is now wholly within the updated summary buffer period ΔtBending at τ2, and the signal properties p1, p2, . . . , pPof signal S2are added to the summary data structures SUM of both leaf summary nodes SD(0,1) and SD(0,2) which are within the summary buffer and with overlap S2(step S36). Carry-over count is added to the summary data structure SUM of SD(0,2) because S2is carried over from SD(0,1).

Signal S3ends at time t3, at which time the summary buffer covers from t0to τ2. Applying the method ofFIGS.11and12, the buffer is expanded by adding SD(0,3), and the signal properties p1, p2, . . . , pPof signal S3are added to the summary data structure of SD(0,3). Signal S4ends at time t4, and again the summary buffer is expanded by adding SD(0,4). The summary buffer is now filled, and any further additions will involve removing SD(0,1) from the end. The signal properties p1, p2, . . . , pPof signal S4are added to the summary data structures of both SD(0,3) and SD(0,4) which overlap the signal, with SD(0,4) also including a carry-over count CO. Signal S5ends at time t5, which remains within the summary buffer period of ΔtBand the signal properties p1, p2, . . . , pPof signal S5are added to the summary data structure of SD(0,4).

Signal S6ends at time t6, after the end of the summary buffer at T4. New leaf summary nodes SD(0,5) and SD(0,6) are added iteratively before signal S6fits within the new summary buffer covering SD(0,3) to SD(0,6), and the signal properties p1, p2, . . . , pPof signal S4are added to the summary data structures of each of SD(0,4), SD(0,5) and SD(0,6) which overlap the signal. When SD(0,1) is removed from the end of the summary buffer, level n=1 node SD(1,1) is opened and the summary data structure SUM of SD(0,1) is copied over. When SD(0,2) is subsequently shifted from the end of the summary buffer, the summary data structure SUM is propagated, but will not change the data of SD(1,1), because the carry-over value CO of signal S2will cancel the count value C and prevent double counting of signal S2in SD(1,1).

Signal S7ends at time t7, coinciding with the end of the summary buffer at τ4. However, referring in particular toFIG.13C, since signal S7ends within the summary buffer and started before it (step S35|No followed by step S37|No), the signal data14of signal S7are instead stored to the auxiliary database AD.

The tree structure of summary nodes SD(n,mn) continues to be built up in this way, as shown inFIG.13B. When the reception of the time series of radio data Erfinishes, the remaining leaf summary nodes SD(o,mn) in the summary buffer are propagated, one at a time from oldest to newest, followed by adding any summary nodes SD(n,mn) necessary to complete the tree structure with a single root node SD(N−1,1).

First Method of Querying the Summary Database

Referring also toFIG.14, a process flow diagram is shown for a method of processing a query using a summary database SD corresponding to the summary database SD structured as illustrated inFIGS.9and10and produced using the method ofFIGS.11and12.

When processing the query using the summary database SD (i.e. following step S11|Yes inFIG.5), the query processing level nQof the summary database SD tree structure is determined based on the query time period ΔtQ(step S53). The query processing level nQmay be a level of the summary database SD tree structure for which the time period ΔtS(nQ,mn) corresponding to each summary node has the minimum difference to a predetermined or dynamically determined fraction of the query time period ΔtQ. For example, the predetermined fraction may be 1/50 and 1/1000, so that the query processing level nQmay correspond to a level at which the time period ΔtS(nQ,mn) corresponding to each summary node is closest to equaling the predetermined fraction of the query time period ΔtQ.

For each summary node SD(nQ,mnQ) at the query processing level nQhaving a summary node period ΔtS(nQ,mn) overlapping the query time period ΔtQ, any entry in the corresponding summary data structure SUM which matches the query fields, tstart, tend, and any specified ranges Δp1, Δp2, . . . , ΔpP, is added to the summary query result qSres(step S54). The search in time can only use the summary node time data tSstart(n,mn), tSend(n,mn), ΔtS(nQ,mn) because the summary nodes SD(nQ,mnQ) do not store the individual signal start and end times tstart, tend.

The auxiliary database AD is also searched to find any signal data14stored therein and matching the query fields (step S55). Any matches are processed to add the signal data to the summary query result qSres.

The summary query result qSresis output (step S56) for display/storage etc (step S14).

In this way, the summary database SD may be quickly searched at a query level nQwhich is set based on the query time period ΔtQ, meaning that the search will be conducted at a pre-existing level of aggregation, saving considerable time and processing operations. For many purposes, in particular but not limited to preparing visualisations such as graphs or heat maps, conducting the query as a finer timescale would in any event be pointless due to finite resolution of an output display and/or a perceptibility to a user.

The summary query result qSresmay take the form of a list. When an entry in the summary data structure SD which matches the query fields is found, a new entry may be added to the list including the unique combination of quantised signal property values p1, p2, . . . , pPcorresponding to that entry and corresponding summary node start tSstart(n,mn) and end tSend(n,mn) times. When signals are added from the auxiliary database AD, a new entry may be added to the list including the signal property values corresponding to that signal data14, and corresponding signal start tstartand end tendtimes, and the count value Cy may be assigned as 1 (one).

The method may include aggregating one or more signal properties stored in the summary query result in the same way as aggregating signal as for signal properties stored in the detailed query result. Aggregating one or more signal properties stored in the summary query result may be additionally weighted based on the count number corresponding to each entry.

Second Method of Storage to the Summary Database

Referring also toFIGS.15and16, a second exemplary method of building the summary database SD is illustrated.

FIG.15shows a process flow diagram of the second exemplary method of building the summary database SD, andFIG.16shows a process flow diagram for a method of propagating data to higher levels n within the tree structure (used at step S93inFIG.15).

The summary nodes generated using the second exemplary method are denoted SD′(n,mn), and are the same as those produced using the first exemplary method (FIGS.11and12), except that the summary data structures SUM do not include carry-over count values COj. The second exemplary method avoids the need for carry-over values and avoids using a summary buffer. Instead, the tree structure is searched (and/or added to) to find the lowest level n at which a detected signal is entirely encompassed by a summary node SD′(n,mn) period ΔtS(n,mn). An auxiliary database AD is still used to store any signals which cannot be fitted into the nodes SD′(n,mn) at each level n (for example because they span a boundary between nodes at that level).

In the second exemplary method, each level of the summary database SD′ tree structure includes one active summary node SD′(n,mn) corresponding to the current maximum value of index mn, and any number of closed summary nodes, SD′(n,mn−1), SD′(n,mn−2) and so forth. The active summary node SD′(n,mn) at each level n is the most recent. In the second exemplary method, the auxiliary database AD′ takes the form of a separate interval structure INT(n) corresponding to each level n of the summary database SD′.

The second exemplary method of building the summary database follows detection of a signal (step S2) and determination of the signal data14(step S4) including the at least the signal start time tstart, the signal end time tend, and the values of the one or more signal properties p1, p2, . . . , pP.

The signal data14is received (step S84). Starting from the lowest level n=0 (step S85), successively increasing levels n of the summary database SD′ tree structure are searched.

If the start tstartand end tendtime of the signal are within the summary node period ΔtS(n,mn) corresponding to the active summary node SD′(n,mn) at the searched level n (step S86Yes), the signal property values p1, p2, . . . , pPof the signal are added to the summary data structure SUM of the active summary node SD′(n,mn) at the searched level n (step S87).

If the start time tstartof the signal is within the summary node period ΔtS(n,mn) corresponding to the active summary node SD′(n,mn) at the searched level n and the end time tendis outside the summary node period ΔtS(n,mn) (step S86No followed by step S88Yes), the active summary node SD′(n,mn) at the searched level n is closed (step S92).

This may involve saving/storing the active summary node SD′(n,mn) to one or more storage devices6and/or marking it as read-only, but equally may not require specific action beyond carrying out the following stages (steps S93to S95). The summary data structure SUM of the summary node SD′(n,mn) is propagated to all higher level summary nodes SD′(>n,mn) from which the summary node SD′(n,mn) at the searched level n is a descendant (step S93). The propagation process may include adding new levels n and/or recursive propagation processes, and shall be explained further in relation toFIG.16hereinafter. A new active summary node SD′(n, mn+1) is added (step S94), and the index mnincremented to mn+1 (step S95) before returning to the initial test (step S86) to determine whether the signal can fit within the updated active summary node SD′(n,mn) (recalling that mnhas been incremented by one).

If the end time tendof the signal is within the summary node period ΔtS(n,mn) corresponding to the active summary node SD′(n,mn) at the searched level n and the start time tstartis outside the summary node period ΔtS(n,mn) (step S86No followed by step S88|No), then the signal data14of the signal is added to the interval structure INT(n) corresponding to the searched level (step S89) and the searched level n is increased by one to n+1 (step S90). The interval structure INT(n) for each level n need not be a tree structure, and alternatively the interval structure INT(n) for each level n may have the same structure as the detailed database DD (and be built the same way).

If the start tstartand end tendtime of the signal are within the summary node period ΔtS(n,mn) corresponding to the active summary node SD′(n,mn) at the increased searched level n (step S91|Yes—recalling that n was incremented in the preceding step), the signal property values p1, p2, . . . , pPof the signal are added to the summary data structure SUM of the active summary node SD′(n,mn) at the incremented searched level n (step S87). If a first summary node SD(n,1) at the incremented level n does not exist, it is generated. However, if the start tstartand end tendtime of the signal still do not fit within the summary node period ΔtS(n,mn) corresponding to the active summary node SD′(n,mn) at the incremented searched level n (step S91|No), the process returns to the second test (step S88).

In this way, each level n is iteratively searched until the signal property values p1, p2, . . . , pPof the signal can be stored to the summary data structure SUM of a summary node SD′(n,mn) encompassing the signal start tstartand end tendtime. At each level below this the signal data14is stored to the respective interval structure INT(n).

Referring in particular toFIG.16the propagation of summary node SD′(n,mn) data to higher levels shall be described.

The method of propagation may be carried out relative to any level n, and indeed is carried out at step S100of this method ofFIG.16. In other words, the process of propagation of data in summary nodes SD′(n,mn) to higher levels n>0 may be recursive as needed.

Data is only propagated to the most recent summary node SD′(n,mn) at each level, with new nodes being added if necessary.

A command is received to propagate data of a node SD′(n,mn) to higher levels (step S96). For example, this is applied to the active summary node SD′(n,mn) at step S93in the second method shown inFIG.15, and to the data of a summary node SD′(n+1,mn+1) at step S100of the propagation method shown inFIG.16. The data to be propagated includes at least the summary data structure SUM, but may include further data in other examples.

It is tested whether the summary node time period ΔtS(n,mn) of the propagated node SD′(n,mn) fits wholly within summary node time period ΔtS(n+1,mn+1) of the most recent summary node SD′(n+1,mn+1) at the next level up n+1 (step S97). In other words, whether tSstart(n+1,mn+1)<tSstart(n,mn)<tSend(n+1,mn+1) and tSstart(n+1,mn+1)<tSend(n,mn)<tSend(n+1,mn+). If a first summary node SD′(n+1,1) at the incremented level n+1 does not exist, it is generated.

If the summary node time period ΔtS(n,mn) of the propagated node SD′(n,mn) wholly fits (step S97|Yes), then the data from the propagated node SD′(n,mn) is added to the node SD′(n+1,mn+1) (step S98) and the process ends. In other words, the summary data structure SUM of propagated node SD′(n,mn) is added to the summary data structure SUM of node SD′(n+1,mn+1). If the same unique set of signal properties p1, p2, . . . , pPexists in both, the entries are merged by combining the count values Cy.

When the summary node time period ΔtS(n,mn) of the propagated node SD′(n,mn) does not wholly fit within the summary node time period ΔtS(n+1,mn+1) of the most recent summary node SD′(n+1,mn+1) at the next level up n+1 (step S97|No), the most recent summary node SD′(n+1, mn+1) at level n+1 is closed (step S99). This may involve writing the summary node SD′(n+1, mn+1) to a storage device6or flagging as read only, but in practice may not require doing anything specific beyond carrying out the following steps (S100to S102). The data in summary node SD′(n+1, mn+1) is propagated to levels n+2 and higher (step S100), which is in practice a command (step S96) to propagate node SD′(n+1, mn+1). Once data has been propagated to levels n+2 and higher (step S100), the most recent summary node SD′(n+1,mn+1) is shifted by adding the next summary node SD′(n+1,mn+1+1) (step S101) and incrementing the index mn+1→mn+1+1 (step S102) before returning to the initial test (step S97).

Alternatively, any other suitable method for propagating the summary data structures SUM to higher levels n may be used (provided that it does not result in double counting).

Referring also toFIGS.17A to17C, a worked example of applying the second exemplary method of building the summary database SD will be explained.FIG.17AreproducesFIGS.6A and13Afor convenience in comparingFIGS.17B and17C.FIG.17Bschematically illustrates the tree structure of the summary database SD generated for the signals shown inFIG.17Ausing the second exemplary method, andFIG.17Cschematically illustrates the auxiliary database of the summary database SD generated for the signals shown inFIG.17Ausing the second exemplary method.

InFIGS.13B and13C, the notation S4,5,6denotes for brevity the signals S4, S5and S6. Similarly, S6,8denoted signals S6and S8, and so forth.

Initially, only one summary leaf node SD′(0,1) exists. At time t1=τ1, the first signal S1ends. The index mo=1 and signal S1is wholly within the period ΔtS(0,1) (step S86|Yes), so the signal properties p1, p2, . . . , pPof signal S1are added to the summary data structure SUM of leaf summary node S(0,1) (step S87).

The second signal S2does not wholly fit in SD′(0,1) and ends after it (step S86|No followed by step88|Yes), the data is propagated (step S93), in the process generating level n=1 node SD′(1,1), and the next summary leaf node SD′(0,2) is generated (step S94). The second signal S2also does not wholly fit in SD′(0,2) (step S86No followed by step88|Yes), and so the signal is added to the interval structure INT(0) (step S89) and the n=1 level is searched (step S90). With n=1, it is found that the signal S2first within node SD′(1,1) (step S91|Yes) and the signal properties p1, p2, . . . , pPof signal S2are added to the summary data structure SUM of summary node SD′(1,1) (step S87).

The process continues in this way through signals S3to S11. Referring in particular toFIGS.17B and17C, it may be observed that whilst many signals may need to be stored to the interval structure INT(o) at the lowest level n=0, the number of signals quickly drops with increasing level n. Compared to the auxiliary database AD of the first exemplary method, the approach of the second exemplary method means that at longer query time periods ΔtQ, which will generally be processed at higher query levels nQ, the additional complexity of searching the auxiliary database AD may be reduced or even avoided.

Second Method of Querying the Summary Database

Referring also toFIG.18, a process flow diagram is shown for a method of processing a query using a summary database SD′ corresponding to the summary database SD′ produced using the method ofFIGS.15and16.

When processing the query using the summary database SD′ (i.e. following step S11|Yes inFIG.5), the query processing level nQof the summary database SD′ tree structure is determined based on the query time period ΔtQ(step S104). This is the same as the corresponding step S53shown inFIG.14.

For each summary node SD′(nQ,mnQ) at the query processing level nQhaving a summary node period ΔtS(nQ,mn) overlapping the query time period ΔtQ, any entry in the corresponding summary data structure SUM which matches the query fields, tstart, tend, and any specified ranges Δp1, Δp2, . . . , ΔpP, is added to the summary query result qSres(step S105). This is the same as the corresponding step S54shown inFIG.14. The summary query result qSresis structured in the same way as described in relation toFIG.14, and may be used in the same way(s).

After pulling relevant data from the summary nodes SD′(nQ,mnQ), the auxiliary database AD is then also searched to find any signal data14stored therein and matching the query fields (step S1o6). Unlike the corresponding step S55shown inFIG.14, only the interval structure INT(nQ) at the search level nQneeds to be searched. Any matches are processed to add the signal data to the summary query result qSres.

The summary query result qSresis output (step S107) for display/storage etc (step S14). This is the same as the corresponding step S56shown inFIG.14.

Third Method of Storage to the Summary Database

Referring also toFIGS.19to21, a third exemplary method of building the summary database SD is illustrated.

FIG.19schematically illustrates a summary node SD*(n,mn) used in the third exemplary method. The summary node SD*(n,mn) is the same as the summary node SD(n,mn) shown inFIG.10, except that the summary data structure SUM does not include carry-over count values COj, and that the summary node SD*(n,mn) also includes a propagation list15, for example in the form of signal data141,142, . . . ,141for a number J of signals (further explained hereinafter).

FIG.20shows a process flow diagram of the third exemplary method of building the summary database SD, andFIG.21shows a process flow diagram for a method of propagating data to higher levels n within the tree structure (used at step S65inFIG.20).

In the third exemplary method, each level of the summary database SD* tree structure includes one active summary node SD*(n,mn) corresponding to the current maximum value of index mn, and any number of closed summary nodes, SD*(n,mn−1), SD*(n,mn−2) and so forth. The active summary node SD*(n,mn) at each level n is the most recent. In the third exemplary method, the auxiliary database AD* takes the form of a separate interval structure INT(n) corresponding to each level n of the summary database SD*, in the same way as for the summary database SD′ built using the second method.

The third exemplary method of building the summary database follows detection of a signal (step S2) and determination of the signal data14(step S4) including the at least the signal start time tstart, the signal end time tend, and the values of the one or more signal properties p1, p2, . . . , pP. The third exemplary method, like the second, avoids use of a summary buffer, but unlike the second keeps the processing focused on the leaf level n=0 until propagation of data to higher levels n>0 is needed. In some signal environments, in particular if a relatively larger number of signals require propagation to higher levels n, the third exemplary method avoids the need to frequently switch addressed memory locations compared to the second exemplary method. In such environments, the third exemplary method may have superior efficiency.

The signal data14is received (step S58), and it is tested whether or not the signal start tstartand end tendof the signal are wholly within the leaf summary node period ΔtS(n,mn) of the active leaf summary node SD*(0,mo) (step S59). If it is (step S59Yes), then the signal property values p1, p2, . . . , pPof the signal are added to the summary data structure SUM of the active leaf summary node SD*(o,mo) (step S60) and the processing returns to the next signal or buffer BuffR (step S7inFIG.2).

However, if the signal start tstartand end tendof the signal are not wholly within the leaf summary node period ΔtS(n,mn) of the active leaf summary node SD*(0,mo) (step S59|No), it is tested whether the end time tendof the signal is after the end tSend(0,mo) of the active leaf summary node SD*(0,mo) (step S61). If it does not (step S61|No), i.e. if the start time tstartis before the summary node period ΔtS(n,mn) of the active leaf summary node SD*(0,mo), then the signal data14of the signal is added to the interval structure INT(n) corresponding to leaf n=0 (step S62) and the signal data14of the signal is also added to the propagation list15of the active leaf summary node SD*(0,mo) (step S63).

Alternatively, if the signal start tstartand end tendof the signal are not wholly within the leaf summary node period ΔtS(n,mn) of the active leaf summary node SD*(0,mo) (step S59|No), and the end time tendof the signal is after the end tSend(0,mo) of the active leaf summary node SD*(0,mo) (step S61|Yes), then the active leaf summary node SD*(o,mo) is closed (step S64). This may involve saving/storing the active leaf summary node SD*(n,mn) to one or more storage devices6and/or marking it as read-only, but equally may not require specific action beyond carrying out the following stages (steps S65to S67). The summary data structure SUM of the leaf summary node SD*(o,mo) is propagated to all higher level summary nodes SD*(n>0,mn) from which the leaf summary node SD*(o,mo) is a descendant (step S65). The propagation process may include adding new levels n and/or recursive propagation processes, and shall be explained further in relation toFIG.21hereinafter. A new active leaf summary node SD*(0, mo+1) is added (step S66), and the index m, incremented to mo+1 (step S67) before returning to the initial test (step S59) to determine whether the signal can fit within the updated active leaf summary node SD*(o,mo) (recalling that mohas been incremented by one).

Referring in particular toFIG.21the propagation of summary node SD*(n,mn) data to higher levels shall be described.

The method of propagation may be carried out relative to any level n, and indeed is carried out at step S73of this method ofFIG.21. In other words, the process of propagation of data in summary nodes SD*(n,mn) to higher levels n>0 may be recursive as needed.

Data is only propagated to the most recent summary node SD*(n,mn) at each level n, with that node being added if necessary.

A command is received to propagate data of a node SD*(n,mn) to higher levels (step S68). For example, this is applied to the active leaf summary node SD*(0,mo) at step S65in the third method shown inFIG.20, and to the data of a summary node SD*(n+1,mn+1) at step S73of the propagation method shown inFIG.21. The data to be propagated includes at least the summary data structure SUM, but may include further data in other examples.

It is tested whether the summary node time period ΔtS(n,mn) of the propagated node SD*(n,mn) fits wholly within summary node time period ΔtS(n+1,mn+1) of the most recent summary node SD*(n+1,mn+1) at the next level up n+1 (step S69). In other words, whether tSstart(n+1,mn+1)<tSstart(n,mn)<tSend(n+1,mn+1) and tSstart(n+1,mn+1)<tSend(n,mn)<tSend(n+1,mn+). If a first summary node SD*(n+1,1) at the increased level n+1 does not exist, it is generated.

If the summary node time period ΔtS(n,mn) of the propagated node SD*(n,mn) wholly fits (step S69|Yes), then the data from the propagated node SD*(n,mn) is added to the node SD*(n+1,mn+) (step S70). In other words, the summary data structure SUM of propagated node SD*(n,mn) is added to the summary data structure SUM of node SD*(n+1,mn+1). The propagation list15of the propagated node SD*(n,mn) is then accessed (step S71) and processed (steps S76though S82) as described hereinafter.

However, if the summary node time period ΔtS(n,mn) of the propagated node SD*(n,mn) does not wholly fit within the summary node time period ΔtS(n+1,mn+1) of the most recent summary node SD*(n+1,mn+1) at the next level up n+1 (step S69|No), the most recent summary node SD*(n+1, mn+1) at level n+1 is closed (step S72). This may involve writing the summary node SD*(n+1, mn+1) to a storage device6or flagging as read only, but in practice may not require doing anything specific beyond carrying out the following steps (S73to S74). The data in summary node SD*(n+1, mn+1) is propagated to levels n+2 and higher (step S73), which is in practice a command (step S68) to propagate node SD*(n+1, mn+1). Once data has been propagated to levels n+2 and higher (step S73), the most recent summary node SD*(n+1,mn+) is shifted by adding the next summary node SD*(n+1,mn+1+1) (step S74) and incrementing the index mn+1→mn+1+1 (step S75) before returning to the initial test (step S69).

Returning to the processing of the propagation list15of a propagated node SD*(n,mn) (steps S76though S82), if a propagation list15contains an integer number J≥0 of signal data141,142, . . . ,14j, . . . ,14J, then starting with the first j=1 (step S76), it is checked whether the signal corresponding to signal data14jfits wholly within the summary node time period ΔtS(n+1,mn+1) of the most recent summary node SD*(n+1,mn+1) at level n+1 (step S77). This amounts to checking, for the signal corresponding to signal data14j, whether tSstart(n+1,mn+1)≤tstart and tend≤tSend(n+1,mn+1). If it does (step S77|Yes), the signal properties p1, p2, . . . , pPof the signal data14jare added to the summary data structure SUM of the active summary node SD*(n+1,mn+1) (step S78).

However, if the signal corresponding to signal data14jdoes not fit wholly within the summary node time period ΔtS(n+1,mn+1) of the most recent summary node SD*(n+1,mn+1) at level n+1 (step S77|No), then the signal data14jof that signal is added to the interval structure INT(n+1) of the level n+1 (step S79) and the signal data14jof that signal is also added to the propagation list15of the summary node SD*(n+1,mn+1) (step S80). If there are further signal data14jto process (step S81|No), then the index j is incremented to j+1 and the processing repeated (steps S77to S81).

Alternatively, any other suitable method for propagating the summary data structures SUM to higher levels n may be used (provided that it does not result in double counting).

In this way, signal data14of signals which cannot fit into the summary nodes SD*(n,mn) are any level n are stored in the propagations lists15, and then subsequently added to the summary nodes SD*(n,mn) at the first level n where they fit in the corresponding summary node time period ΔtS(n+1,mn+1) during the process of propagating data to higher levels.

The summary databases SD* produced by the third exemplary method have the same structure as a summary database SD′ produced by the second exemplary method, aside from the addition of propagation lists15. Thus, a worked example similar toFIGS.17A to17Cis not repeated. Searching a summary databases SD* produced by the third exemplary method have the same structure as a summary database SD′ produced by the second exemplary method, and so the searching process may be the same as described in relation toFIG.18.

MODIFICATIONS

It will be appreciated that various modifications may be made to the embodiments hereinbefore described. Such modifications may involve equivalent and other features which are already known in the design and use of methods and apparatuses for receiving, storing and/or processing time-series of data, and which may be used instead of or in addition to features already described herein. Features of one embodiment may be replaced or supplemented by features of another embodiment.

In some examples, the apparatus3may omit the communications interface6, for example when only a local client10is required.

Although claims have been formulated in this application to particular combinations of features, it should be understood that the scope of the disclosure of the present invention also includes any novel features or any novel combination of features disclosed herein either explicitly or implicitly or any generalization thereof, whether or not it relates to the same invention as presently claimed in any claim and whether or not it mitigates any or all of the same technical problems as does the present invention. The applicants hereby give notice that new claims may be formulated to such features and/or combinations of such features during the prosecution of the present application or of any further application derived therefrom.